Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

The urban mangrove of the Vitória Bay, Espírito Santo, Southern Brazil suffers from anthropogenic impacts, which interfere in the foliar spectral response of its species. Identifying the spectral behavior of these species and creating regression models to indirectly obtain structure data like the Leaf Area Index (LAI) are powerful environmental monitoring tools. In this study, LAI was obtained in 32 plots distributed in four stations. In situ LAI regression analysis with the SAVI resulted in significant positive relationships (r² = 0.58). Forest variability regarding the degree of maturity and structural heterogeneity and LAI influenced the adjustment of vegetation indices (VIs). The highest regression values were obtained for the homogeneous field data, represented by R. mangle plots, which also had higher LAI values. The same field data were correlated with SAVI of a RapidEye image for comparison purposes. The results showed that, images obtained by a UAV have higher spatial resolution than the Rapideye image, and therefore had a greater influence of the background. Another point is that the statistical analysis of the field data with the IVs obtained from the RapidEye image did not present high regression coefficient (r² = 0.7), suggesting that the use of VIs applied to the study of urban mangroves needs to be better evaluated, observing the factors that influence the leaf spectral response.

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France) which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May-October period which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016-2020) was used. The detection mowing number was done at the pixel level and then results are aggregated at the plot level. A validation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94-0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands.

Leaf mass per area (LMA) is a key structural parameter that reflects the functional traits of leaves and plays a vital role in simulating the material and energy cycle of plant ecosystems. In this study, vertical whorl-by-whorl sampling of LMA was conducted in a young Larix principis-rupprechtii plantation during the growing season at the Saihanba Forest Farm. The vertical and seasonal variations in LMA were analysed. Subsequently, a predictive model of LMA was constructed. The results revealed that the LMA varied significantly between different crown whorls and growing periods. In the vertical direction of the crown, the LMA decreased with increasing crown depth, but the range of LMA values from the tree top to the bottom was, on average, 30.4 g/m2, which was approximately 2.5 times greater in the fully expanded phase than in the early leaf-expanding phase. The LMA exhibited an allometric growth trend that increased during the leaf-expanding phase and then tended to stabilize. However, the range of LMA values throughout the growing period was, on average, 40.4 g/m2. Among the univariate models, the leaf dry matter content (LDMC) performed well (Ra2=0.45, RMSE=13.48 g/m2) in estimating the LMA. The correlation between LMA and LDMC significantly differed at different growth stages and at different vertical crown whorls. The dynamic predictive model of LMA constructed with the RDINC and DOY as independent variables was reliable in both the assessments (Ra2=0.68, RMSE=10.25 g/m2) and the validation (MAE=8.05 g/m2, FI=0.682). Dynamic simulations of crown LMA provide a basis for elucidating the mechanism of crown development and laying the foundation for the construction of an ecological process model..

Invasive alien plant species (IAPS) pose one of the most significant threats to native biodiversity. Swietenia macrophylla, or big leaf mahogany, is among the most threatening invasive plants in the Philippines. This article aimed to formally document the presence of S. macrophylla along the edges of Mt. Banahaw de Nagcarlan, a protected area on Luzon Island, Philippines. The study also sought to identify the management strategies being implemented by various government institutions for addressing big leaf mahogany and other invasive plants. A total of 1591 individuals of S. macrophylla were documented in mixed land-use areas and roadsides. These were found to have been introduced by the Department of Environment and Natural Resources in 1991 as a reforestation species. Fortunately, no individuals were observed beyond the buffer zone towards the protected area. The identification of management strategies for big leaf mahogany and other IAPS revealed that there is no established approach specifically addressing the presence of S. macrophylla at the site. However, some institutions advocate for the conservation of native plants through tree planting activities and educational campaigns. Furthermore, no collaborative efforts were observed among stakeholders and institutions. The results of this study highlight the urgent need for managing the S. macrophylla population. Planning and enforcement of strategies require collaborative efforts among stakeholders to prevent its entry into the protected area and ensure the preservation of native biodiversity.

The amount and spatial distribution of foliage in a tree canopy have fundamental functions in ecosystems as they affect energy and mass fluxes through photosynthesis and transpiration. They are usually described by the Leaf Area Index (LAI) and the Leaf Area Density (LAD), which can be measured through a variety of methods, including voxel-based methods applied to LiDAR point clouds. A theoretical study recently compared the numerical errors arising from different voxel-based estimation methods for Plant Area Density (PAD) based on Beer’s law-based, contact frequency and Maximum-Likelihood Estimation, showing that the bias-corrected Maximum Likelihood Estimator was theoretically the most efficient. However, this earlier study i) ignored wood volumes; ii) neglected vegetation clumping inside the voxel; iii) ignored instrument characteristics in terms of effective footprint, iv) was limited to a single viewpoint. In practice, retrieving LAD from PAD is not straightforward, vegetation is not randomly distributed in volumes of interest, beams are divergent and forestry plots are usually sampled from more than one viewpoint, to mitigate the effect of occlusion. In the present short communication, we extend the previous efficient formulation to actual field conditions to i) account for the presence of both wood volumes and wood hits, ii) rigorously include correction terms for vegetation and instrument characteristics, iii) integrate multiview data. A numerical comparison with other methods commonly used to combine information from different viewpoints led to error reduction, especially in poorly-explored volumes, which are frequent in actual canopies. Beyond its concision, completeness and efficiency, this new formulation -which can be applied to multiview TLS, but also UAV LiDAR scanning - can help reducing errors in LAD estimation.

Rigid-body visual tracking is an active research field with many practical applications including visual surveillance and intelligent transport system. In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to detect and track each leaf of a plant for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking and generation of a leaf status report. The proposed method uses a graph theoretic approach to reliably detect and track individual leaves by overcoming the challenge of leaf-losses based on temporal image sequence analysis for automatically generating the leaf status report containing the following phenotypes, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf stopped growing, and the length and relative growth rate of individual leaves. The proposed method demonstrates high accuracy in detecting leaves and tracking them through the early vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.

Drought, a crucial abiotic stressor, markedly reduces the growth and yield of tomato crops (Solanum lycopersicum L.). Consequently, adopting drought-resistant cultivars and implementing breeding programs to enhance drought tolerance have emerged as enduring solutions to alleviate the adverse effects of drought in various tomato cultivation regions. In this study, 68 tomato accessions from the United States Department of Agriculture (USDA) were assessed in a controlled greenhouse experiment, encompassing both water deficit treatment and a control group subjected to standard watering conditions. The experiment was arranged in a randomized complete block design with three replications. The results of this study pinpointed four accessions PI 365956, PI 584456, PI 390510, and PI 370091, as drought-tolerant accessions. Additionally, high broad-sense heritability was revealed for leaf wilting, leaf rolling, and SPAD chlorophyll content (total leaf chlorophyll). Furthermore, positive correlations were found among parameters associated with leaf wilting, leaf rolling, and SPAD chlorophyll content. The findings of this study provide valuable insights relevant to tomato breeding initiatives, particularly those aimed at fortifying drought tolerance within the elite cultivars of this essential crop.

Leaf color mutants (LCMs) are important resources for studying diverse metabolic processes such as chloroplast biogenesis and differentiation, pigments’ biosynthesis and accumulation, and photosynthesis. However, in Dendrobium officinale, LCMs are yet to be fully studied and exploited due to the unavailability of reliable RGs (reference genes) for qRT-PCR (quantitative real-time reverse transcription PCR) normalization. Hence, this study took advantage of previously released transcriptome data to select and evaluate the suitability of ten candidate RGs, including Actin (Actin), polyubiquitin (UBQ), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), elongation factor 1-α (EF1α), β-tubulin (β-TUB), α-tubulin (α-TUB), 60S ribosomal protein L13-1 (RPL13AD), aquaporin PIP1-2 (PIP1-2), Intima protein (ALB3) and Cyclin (CYCB1-2) for normalizing leaf color-related genes’ expression levels via qRT-PCR. Stability rankings analysis via common software Best-Keeper, GeNorm, and NormFinder disclosed that both ten genes met the requirements of RGs. Of them, EF1α exhibited the highest stability and was selected as the most reliable. The reliability and accuracy of EF1α were confirmed through qRT-PCR analysis of fifteen chlorophyll pathway-related genes. The expression patterns of these genes via EF1α normalization were consistent with the results by RNA-Seq. Our results will pave the way for molecular dissection of leaf color mutations in D. officinale.

The circular economy with compost fertilization is included in the sustainable orchard paradigm, creating a holistic production ecosystem. Modern orchards are mostly intensive and super-intensive, requiring different rootstocks. This research presents the response to compost fertilization of two specific pear rootstocks (quince ‘CTS 212’ and ‘Farold® 40’) and own-rooted trees, analyzing six resistant cultivars in a circular production system. The dynamic of nitrogen and carbon content in leaves, soil respiration coefficient, the evolution of the fruit maturity stage in the field, and some biometric parameters such as trunk cross-section area, the annual vegetative growth, and fruiting shoots annual number were analyzed. The results highlighted the effect of compost fertilization on plants: almost all variants presented higher nitrogen amounts in leaves in the first and second years. Carbon content in leaves had a lower dynamic over time. Rootstock and compost fertilization influenced the fruit maturity dynamic, but a single pattern was not identified. Quince as pear rootstock expressed a higher sensitivity to compost application, the biometric parameters as trunk cross-section area, and almost all cultivars' annual vegetative growth were higher than the control's. Positive output can lead to future model upscaling in the farms and households.

Tomato leaf curl disease (TLCD) is the most common viral disease in the tomato plant. It is caused by begomoviruses, which are viruses that cause plant development to be slowed. Many of the traditional disease management methods are still in use. They are, however, ineffective and out of date. Modern biotechnology is being used to detect illness in tomato plants as early as possible, thus reducing damage to the plants. Through genetic engineering, the spread of viruses may be controlled or prevented entirely. Here reviewed many methods for decreasing or eliminating the viral influence on crop growth through biotechnology and genomics. We also investigated the possibility of genetic engineering to reduce or remove the virus TLCD impact on tomato crop development.

Among the rust diseases, leaf rust of wheat caused by Puccinia triticina, is the most prevalent worldwide and causes significant yield losses. This study aimed to determine the genomic location of loci that control adult plant resistance (APR) to leaf rust in the pre-Green Revolution landrace accession, Aus27506, from the ‘Watkins Collection’. An Aus27506/Aus27229-derived F7 recombinant inbred (RIL) population was screened under field conditions across three cropping seasons and genotyped with the iSelect 90K Infinium SNP bead chip array. One QTL on each of chromosomes 1BL, 2B and 2DL explained most of the leaf rust response variation in the RIL population and were named QLr.sun-1BL, QLr.sun-2B and QLr.sun-2DL, respectively. QLr.sun-1BL and QLr.sun-2DL were contributed by Aus27506. QLr.sun-1BL is likely Lr46, while QLr.sun-2DL appeared to be a new APR locus. The alternate parent, Aus27229, carried the putatively new APR locus QLr.sun-2B. Comparisons of average severities among RILs carrying these QTL in different combinations indicated that QLr.sun-2B does not interact with either of the other two QTL; however, the combination of QLr.sun-1BL and QLr.sun-2DL reduced disease severity significantly. In-planta fungal quantification assays validated these results. The RILs carrying QLr.sun-1BL and QLr.sun-2DL did not differ significantly from parent Aus27506 in resistance. Aus27506 can be used as a source of adult plant leaf rust resistance in breeding programs.

Management practices must be developed to improve yam production sustainability. Image-based phenotyping techniques could help developing such practices based on non-destructive analyses of important plant traits. Our objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in glasshouse and field. We took plant and leaf pictures with consumer cameras. We used the numbers of image pixels to derive the shoot biomass and the total leaf surface and calculated the ‘triangular greenness index’ (TGI) which is an indicator of the plant nitrogen (N) nutritional status. Under glasshouse conditions, the number of pixels obtained from nadir view (image taken top down) was positively correlated to the shoot biomass, and the total leaf surface, while the TGI was negatively correlated to the N content of diagnostic leaves. Under field conditions, pictures taken from the nadir view showed an increase in soil surface cover and a decrease in TGI with time. TGI was negatively correlated to SPAD measured on specific leaves but was not correlated to the N content of these leaves. In conclusion, these phenotyping techniques deliver relevant results but need to be further developed and validated for application in yam.

Water plays a crucial role in maintaining plant functionality and drives many ecophysiological processes. The distribution of water resources is in a continuous change due to global warming affecting the productivity of ecosystems around the globe, but there is a lack of non-destructive methods capable of continuous monitoring of plant and leaf water content that would help us in understanding the consequences of the redistribution of water. We studied the utilization of novel small hyperspectral sensors in the 1350-2450 nm spectral range in non-destructive estimation of leaf water content in laboratory and field conditions. We found that the sensors captured up to 96% of the variation in equivalent water thickness (EWT, g/m²) and up to 90% of the variation in relative water content (RWC). These laboratory findings were supported by field measurements, where repeated leaf spectra measurements were in good agreement (R²=0.79) with a time-lagged change of tree xylem diameter. Further tests were done with an indoor plant (Dracaena marginate Lem.) by continuously measuring leaf spectra while drought conditions developed, which revealed detailed diurnal dynamics of leaf water content. We conclude that close-range hyperspectral spectroscopy can provide a novel tool for continuous measurement of leaf water content at the single leaf level and help us to better understand plant responses to varying environmental conditions.

Current agriculture intensifies crop cultivation to meet food demand, leading to unsustainable use of chemical fertilizers. This study investigates a few physiological and agronomic responses of common wheat following the inoculation with plant growth-promoting bacteria to reduce nitrogen inputs. A field trial was conducted in 2022-23, in Legnago (Verona, Italy) on Triticum aestivum var. LG-Auriga comparing full (180 kg ha-1) and reduced (130 kg ha-1) N doses, both with and without foliar application at end tillering of the N-fixing bacterium Methylobacterium symbioticum. Biofertilization did not improve shoot growth, while it seldom increased root length density in the arable layer. It delayed leaf senescence, prolonged photosynthetic activity, amplified stomatal conductance and PSII efficiency under reduced N dose. Appreciable ACC-deaminase activity of such bacterium disclosed augmented nitrogen retrieval and reduced ethylene production, explaining ameliorated stay-green. Yield and testing weight were unaffected by biofertilization, while both glutenin-to-gliadin and HMW-to-LMW ratios increased together with dough tenacity. It is concluded that Methylobacterium symbioticum can amplify nitrogen metabolism at reduced nitrogen dose, offering a viable approach to reduce chemical fertilization under suboptimal growing conditions for achieving a more sustainable agriculture. Further research over multiple growing seasons and soil types is necessary to corroborate these preliminary observations.

Invasive species and plant diseases are critical threats to ecosystems and agriculture worldwide. Effective early detection of these threats can significantly mitigate their impact on biodiversity and crop yields. This paper presents a neural network-based model for detecting leaf diseases and identifying invasive species using advanced image processing techniques. The model integrates edge detection, color analysis, and morphological feature extraction to assess leaf health and species type. By automating the identification process, this approach offers an efficient and scalable solution for real-time ecological monitoring, contributing to conservation and agricultural sustainability efforts.

This study employed High-Performance Liquid Chromatography coupled with Diode Array De-tector and Mass Spectrometry (HPLC-DAD-MS) to characterize bioactive compounds in olive leaf extracts. The analysis revealed a diverse metabolic profile, predominantly composed of tyrosol, flavonoids, and hydroxycinnamic acid. The antioxidant activity of the extracts demonstrated their efficacy, with variations observed between neutral and acidic pH conditions. Encapsulation was explored as a preservation method, successfully maintaining the integrity of phenolic compounds in both liquid and powdered forms. All four extracts have an undeniable antimicrobial effect. Moreover, the antibacterial activity tested on E. coli ATCC 25922 and S. aureus ATCC 25923 strains significantly improved post-encapsulation, particularly for the acidic pH extract. The findings suggest that encapsulation can potentially conserve and/or enhance the antimicrobial activity of olive leaf extract, paving the way for developing functional products with improved shelf life and nutritional properties. Further investigations are recommended to elucidate underlying mecha-nisms and optimize encapsulation processes to maximize the benefits of olive leaf extract in di-verse applications.

Maize, a crop of international relevance, frequently undergoes xylem embolism due to water shortage, negatively impacting growth, yield, and quality. Consequently, a refined comprehension of xylem embolism is vital for enhancing maize cultivation. Notwithstanding extensive research and the generation of analytical models for embolism mechanisms, prevalent models often disregard crop-specific hydraulic processes and the formation of embolisms via air bubbles in the xylem conduit. In this research, we present an inventive model applying pipe cavitation parameters to discern water potential and bubble formation in maize leaf xylem. The model integrates pivotal physiological traits of maize—leaf count, leaf vein count, and average leaf radius—demonstrating robust correlations. Furthermore, we constructed a percentage conductivity loss (PLC) curve based on pre-dawn leaf water potential and compared it with our model, offering interval data to observe embolisation events triggered by air bubbles. Utilizing experimental data, our novel cavitation-parameters based model effectively corresponds with observed bubble phenomena and appropriately characterizes water transport in plant xylem conduits. This method enabled us to observe the transition from bubble occurrence to cavitation embolism microscopically, which aligned with the embolism intervals provided by the model. This procedure reveals potential trends in bubble-induced embolism and deepens our knowledge of microscopic plant hydraulics and crop embolism. This work establishes a basis for understanding the generation of bubble embolisms in maize, assists in evaluating maize plant water status for efficient water supply management throughout the growth cycle, and contributes towards potential water management strategies for maize.

Mulberry leaves are a classic herb commonly used in traditional Chinese medicine, and experience since ancient times has shown that leaves collected after frost have better medicinal properties. However, the characteristics of the changes of critical metabolic components in mulberry leaves, especially Morus nigra L., are still unknown. To further study the influence of harvesting time on the dynamic changes of metabolites, especially DNJ compounds and flavonoids, in mulberry leaves after frost. We used metabolomics technology to select 100+ metabolites as indexes to reflect the dynamic changes of metabolites in mulberry leaves comprehensively. The results showed significant differences in the effects of defrosting on the accumulation of metabolites in the leaves of Morus nigra L. and Morus alba L. The content of DNJ in the leaves of Morus nigra L. showed a decreasing trend after the frost, while flavonoids reached the peak of the second frost. For Morus alba L, DNJ showed an increasing trend after frost, especially reaching its peak one day after the second frost. Flavonoids mainly peak in the week before the frost. In addition, by analyzing the influence of picking time on metabolite accumulation in two mulberry leaves, it was concluded that higher DNJ alkaloids and flavonoids would be obtained in mulberry leaves collected in the morning. This study also provides scientific guidance for the optimum harvesting time of mulberry leaves.

Drought-induced plant mortality has increased globally during the last decades and is forecasted to influence global vegetation dynamics. Timely information on plant water dynamics is essential for understanding and anticipating drought-induced plant mortality. The most common metric that has been used for decades for measuring water stress is leaf water potential (Ψ_L), which is measured destructively. To obtain information on water dynamics from trees and forested landscapes, remote sensing methods have been developed. However, the spatial and temporal resolution of the existing methods have limited our understanding of water dynamics and diurnal variation of Ψ_L within single trees. Thus, we investigated the capability of terrestrial laser scanning (TLS) intensity in observing diurnal variation in Ψ_L during a 50 hour monitoring period and aimed to improve understanding on how large part of the diurnal variation in Ψ_L can be captured using intensity observations. We found that TLS intensity at 905 nm wavelength was able to explain 78% of the variation in Ψ_L for three trees of two tree species with a root-mean square error of 0.137 MPa. Based on our experiment with three trees, time-series of TLS intensity measurements can be used in detecting changes in Ψ_L, and thus it is worthwhile to expand the investigations to cover a wider range of tree species and forests and further increase our understanding of plant water dynamics at wider spatial and temporal scales.

Plant leaf diseases can affect the plants’ leaves to an extent that the plants can collapse and die completely. These diseases may drastically drop the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the Convolutional Neural Networks (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognized and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and the methodology used, expedite a quick and easy implementation of the system in practice. The developed model was able to recognize 3 different types of maize leaf diseases out of healthy leaves. The Northern Corn Leaf Blight (Exserohilum), Common Rust (Puccinia sorghi) and Gray Leaf Spot (Cerospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.

Oak woodlands are a keystone ecosystem that may not be sustainable under ongoing fire management regimes and climate change. For example, in 2014, sassafras saplings started to emerge in degraded woodlands at the 1200 ha Drury Conservation Area as part of research at the Bull Shoals Field Station in Taney County, Missouri where burning was initiated in 1999. After Spring (March) 2021 burnt, we examined trend in the potential community and ecosystem effects, we assessed sapling density, height, and specific leave weigh (SLW) of white oak and sassafras (<1.0 m) from fall 2021 through fall 2022. In fall 2021, sassafras density was 560/0.1ha with mean height of 25.42cm and SLW of 0.00436g/cm² while white Oak density was 171/0.1ha with mean height of 10.2cm and SLW of 0.00680g/cm. However, in fall 2022, Sassafras density was 582/0.1ha with mean height of 71.689cm and SLW of 0.00366 g/cm² meanwhile, white Oak density was 213/0.1ha with mean height of 50.544cm and SLW of 0.00563 g/cm². We analyzed mass loss for sassafras and white oak after buried for 198 days. The weight loss for sassafras was 0.857g while oak was 0.790g for 61 days of buried. Subsequent sassafras weight loss was 1.942g, 3.343g, 4.222g and 4.82g while that of white Oak was 1.877g, 2.862g, 3.344g and 4.28g for day 105, 153 and 198 respectively. The nutrients composition and carbon sequestration of sassafras and white oak species need to be determined to better understand the effect of their dominance in fire management regime of degraded oak woodland in Southwest Missouri Ozarks.

The main apple production areas of the Loess Plateau, China limited information on soil and leaf K, Ca, Mg, and micronutrients and their relationships in these orchards. Therefore, we collected soil and leaf samples from the main apple production areas of the Loess Plateau and analyzed them to assess soil and leaf K, Ca, Mg, Cu, Mn, Zn, and Fe as well as to visualize their relationships. The investigated apple orchards in the Loess Plateau exhibited low soil Mn, moderate soil K, Zn, Fe, and high Cu and deficiency of leaf Fe, Moderate of leaf K, Cu, and Zn, high of leaf Ca, Mg, and excessive in leaf Mn. Moreover, soil pH negatively correlated with soil K, Cu, Zn, and Fe and positively related to exchangeable Mg, leaf Mg, and leaf Ca. Exchangeable Ca was negatively related to soil Mg, Cu, Mn, and Fe. Meanwhile, exchangeable K also negative correlation with leaf Mg, indicating the importance of soil pH and exchangeable Ca as an indication of apple produc-tion. In addition, available Zn also negative correlation with leaf Ca and leaf Zn. Soil micronu-trient contents in Shaanxi province trend lower than in other provinces, suggesting that soil Mn and Zn deficiency may be a limiting factor for apple production. Consequently, it is suggested that the soil pH be reduced in the Loess Plateau, replenishing soil organic matter and micronutrients, controlling soil Ca and Mg, and the Loess Plateau must pay attention to controlling K fertilizer application to avoid soil exchangeable cation and enhancing organic fertilizer application to improve soil micronutrients (Zn, Mn, and Fe) are highly necessary and it is necessary to enhance leaf Fe.

The development of biofortified and stress tolerant common bean lines contribute to counteract micronutrient malnutrition in the current condition of climate variability. Our objective was to evaluate the adaptive responses of biofortified common bean (Phaseolus vulgaris L.) lines under acidic soils and high temperature stress conditions in the Amazon region of Colombia. Adaptive responses were evaluated based on phenological, physiological and agronomic differences under combined stress conditions. A total of 247 common bean lines from the Mesoamerican gene pool were evaluated under field conditions at Florencia, Caquetá, Colombia. The common bean lines evaluated included 146 from F4 families with high iron (Fe) content and 99 common bean lines from F5 families that were obtained from simple crosses, double crosses and backcrosses among different bean lines. Lines with tolerance to combined stress conditions of acidic soil and high temperature were found with grain yields greater than 1,400 kg ha-1 from the F5 (lines: 859, 805, 865, 657) and F4 (lines: 2853, 2796) families. This improved agronomic response is likely due to the greater partitioning of photosynthates from canopy biomass (CB) toward pod formation (pod partitioning index, PPI) and grain filling (pod harvest index, PHI; harvest index, HI), which translated into higher grain yields (GY). GY was correlated with CB (r = 0.36), PPI (r = 0.6), PHI (r = 0.68), and HI (r = 0.8, P&lt;0.001). The physiological responses that contributed toward superior agronomic performance of biofortified common bean lines include greater allocation of energy to the photosynthetic machinery (ΦII) and its dissipation in the form of heat (ΦNPQ) as the leaf temperature differential (LTD) increased under combined stress conditions. Six biofortified common bean lines (F5 lines: 859, 805, 865, 657; F4 lines: 2853, 2796) were identified with multiple stress resistance traits and these lines can serve as parents for further genetic improvement of common bean for multiple stress tolerance in the Amazon region of Colombia.

Plant phenotype plays an important role in crop breeding and planting. Leaf phenotype is an important part of plant phenotype. In order to analyze the leaf phenotype, the target leaf is required to be segmented from the complex background image. In this paper, an automatic soybean leaf segmentation method based on object detection and interactive segmentation models is proposed. Firstly, the Libra R-CNN object detection algorithm is used to detect all soybean leaves in the image. Then, based on the idea that the target soybean leaf is located in the center of the image and the area is large, the detection bounding box of the target leaf is selected. In order not to destroy the segmentation result, the bounding box is optimized to completely enclose the whole leaf. Finally, according to the optimized bounding box, the prior channels of foreground and background are constructed using Gaussian model. The two channels together with the original image are as the input of the interactive object segmentation with inside-outside guidance model to segment the target soybean leaf. A large number of qualitative and quantitative experimental results show that the method has high segmentation accuracy and strong generalization capacity.

Due to the global warming and dynamic changes in pathogenic virulence, leaf rust caused by Puccinia triticina has greatly expanded its epidermic region and become a severe threat to global wheat production. Genetic bases of wheat resistance to leaf rust mainly relies on the leaf rust resistance (Lr) gene or quantitative trait locus (QLr). Although these genetic loci have been insensitively studied during the last two decades, an updated overview of Lr/QLr in a genome-wide level is urgently needed. This review summarized recent progresses in genetic studies of wheat resistance to leaf rust. Wheat germplasms with great potentials in genetic improvement of resistance to leaf rust were highlighted. Key information about the genetic loci carrying Lr/QLr were summarized. A genome-wide chromosome distribution map for all the Lr/QLr was generated based on the released wheat reference genome. In conclusion, this review has provided valuable sources for both wheat breeders and researchers to understand the genetics of resistance to leaf rust in wheat.

Background, Leucoptera coffeella (Guerin-Meneville, 1842) is a moth species (Lyonetiidae, Lepidoptera) pest that causes severe losses to coffee crops. Further information about its genomic data is required to allow molecular strategies for the development of sustainable pesticides and to gain in-depth knowledge on phylogenetics. However, the closest complete genome available is within the superfamily level (Yponomeutoidea). Here we report the generation of the first long-read genome, transcriptome and proteome results of L. coffeella and the in silico analysis performed in these molecular levels to investigate genes involved in the siRNA processing. Results, PACBio and paired-end Illumina combined DNA sequencing from pupae samples resulted in more than 436 Gb subreads and 31Mb reads with N50 read length of 15,512 nt, mean read length 13.8 Kb and max read length 420.7 Kb. Additionally, 20Gb data of short DNA sequencing was combined to produce 1,984 contigs comprising 397 Mb in total. The longest and shortest scaffold sizes are 10,809,567 nt and 15,247 nt, respectively (mean size 200,178 nt). The N50 scaffold was 275,598 nt and the GC content was 36.10%. Predicted coding DNA sequences counted 39.930 gene models. Searching of 5286 BUSCO groups revealed 91.7 percent of completeness (single and duplicated genes combined) compared to lepidoptera genomes (lepidoptera_odb10). Flow cytometry showed the 1C DNA content is approximately 295 Mb. RNA-Seq from seven development stages resulted in 28294 identified transcripts. Additionally, proteomics from immature stages resulted in 2045 proteins matching the gene models. Conclusions, This first nuclear genome of the Lyonetiidae family brings valuable molecular resources to study Lepidoptera genomes. Genome, transcriptome and proteome sequencing to raise genome annotation precision may resolve uncovered taxonomic issues. In addition, these combined approaches provide insights into plant-insect interaction players, as horizontally transferred genes (HGT) and endosymbionts. Put together, the generated data enables the development of molecular tools towards sustainable biotechnology solutions for lepidopteran pest control.

The onset of leaf senescence is triggered by external cues and internal factors such as phytohormones and signaling pathways involving transcription factors (TFs). Abscisic acid (ABA) strongly induces senescence and endogenous ABA levels are finely tuned by many senescence-associated TFs. Here, we report on the regulatory function of the senescence-induced TF OsWRKY5 TF in rice (Oryza sativa). OsWRKY5 expression was rapidly upregulated in senescing leaves, especially in yellowing sectors initiated by aging or dark treatment. A T-DNA insertion activation-tagged OsWRKY5-overexpressing mutant (termed oswrky5-D) promoted leaf senescence under natural and dark-induced senescence (DIS) conditions. By contrast, a T-DNA insertion oswrky5-knockdown mutant (termed oswrky5) retained leaf greenness during DIS. Reverse-transcription quantitative PCR (RT-qPCR) showed that OsWRKY5 upregulates the expression of genes controlling chlorophyll degradation and leaf senescence. Furthermore, RT-qPCR and yeast one-hybrid analysis demonstrated that OsWRKY5 indirectly upregulates the expression of senescence-associated NAC genes including OsNAP and OsNAC2. Precocious leaf yellowing in the oswrky5-D mutant might be caused by elevated endogenous ABA concentrations resulting from upregulated expression of ABA biosynthesis genes OsNCED3, OsNCED4, and OsNCED5, indicating that OsWRKY is a positive regulator of ABA biosynthesis during leaf senescence. Furthermore, OsWRKY5 expression was significantly suppressed by ABA treatment, indicating negative feedback regulation of OsWRKY5 expression by ABA. OsWRKY5 is a positive regulator of leaf senescence that upregulates senescence-induced NAC genes leading to expression of ABA biosynthesis and chlorophyll degradation genes.

The tea leaf paradox (TLP) describes unsteady fluid motions which help entrain and deposit suspended particles at the center of rotation. Various applications depend on the TLP for particle separations spanning across orders of magnitude in length scales and is an important problem in fluid mechanics. Despite papers describing the phenomena, the efficacy of particle separation using the TLP remains unclear as to the relative importance of for example hydrostatics, particle-fluid density ratio, wall friction, liquid bath aspect ratio and the rotation speed. The dynamics involved are notably complex and require a careful tuning of each variable. In this study, we have investigated the role of the limit of the aggregation dynamics in rotational flows within 3D-printed vessels of various sizes in tandem with particle imaging to probe the dissipation effects on the particle motions. We have found that the liquid bath aspect ratio limits how much aggregation may occur for a particle-fluid density ratio greater than unity (e.g., $\rho_p/\rho_f >1$, where $\rho_p$ is the density of the particle and $\rho_f$ is the ambient fluid density.

Exploring the growth and physiological response mechanisms of rice under continuous drought stress circumstances can provide a significant scientific foundation and technological assistance for meeting drought difficulties, improving drought resistance and rice (Oryza sativa L.) output, and ensuring food security. In this study, a rice field experiment was conducted under a rain shelter with five different treatments set up: P1 (drought stress from tillering stage), P2 (drought stress from jointing-booting stage), P3 (drought stress from heading-flowering stage), P4 (drought stress from grain filling stage), and CK (adequate water management throughout the growth stage). Continuous drought stress from different growth stages with four levels (mild, medium, serious, and extreme). The results showed that the effects of different drought stress treatments on rice growth varied significantly. Compared with the CK treatment, plant height was reduced by 12.10%, 8.14%, 3.83%, and 1.06% in the P1, P2, P3, and P4 treatments, respectively; and the number of tillers was reduced by 23.83%, 18.91%, 13.47%, and 8.68%, respectively. With the increase in drought stress levels, SPAD values and Rubisco activity of rice leaf continued to decrease; SOD activity showed a decreasing trend, but the decreasing trend of POD and CAT activities was not significant, while MDA content showed an increasing trend. For yield components, continuous drought stress significantly reduced spike length, effective number of spikes, thousand grain weight, grain number per spike, and fruiting rate of rice compared to CK treatments during the growth period. In general, continuous drought stress during the early growth period affected the effective spike number and the grain number per spike. Continuous drought stress after the grain filling stage had the least effect on yield and water use efficiency was much higher than other treatments. The results of these studies reveal the response of rice growth and physiology to continuous drought stress, which is important for agricultural practices.

Aiming at the problem of small size of some spots on apple leaves and the difficulty of accurate detection of spot targets brought by the complex background of the orchard, this paper takes the alternaria leaf spot, rust, brown spot, grey spot and frog eye leaf spot on apple leaves as the research object, and proposes a high-accuracy detection model YOLOv5-Res and lightweight detection model YOLOv5-Res4 are proposed. Firstly, a multiscale feature extraction module ResBlock is designed by combining the Inception multi-branch structure and ResNet residual idea. Secondly, a lightweight feature fusion module C4 is designed to reduce the number of model parameters while improving the detection ability of small targets. Finally, a parameter streamlining strategy based on optimized model architecture is proposed. The experimental results show that the performance of YOLOv5-Res model and YOLOv5-Res4 model is significantly improved, the mAP@0.5 value is improved by 2.8% and 2.2% compared with YOLOv5s model and YOLOv5n model, respectively, and the size of YOLOv5-Res model and YOLOv5-Res4 model is only 10.8MB and 2.4MB, and the number of model parameters is reduced compared with YOLOv5s model and YOLOv5n model. counts are reduced by 22% and 38.3% compared to the YOLOv5s model and YOLOv5n model.

Nitrogen is the most commonly managed mineral nutrient in olive groves because it is essential for plant growth. The precise management of N fertilization in olive cultivation is still not fully clarified, but it is now essential for providing sustainable production. A nitrogen fertilizer ex-periment with olive trees (cv. Kalinioti) was carried out over a six-year period. Seven levels of nitrogen fertilizer given as ammonium nitrate (control, 1, 2, 3, 4, 5, and 6 kg/tree) were annually applied in order to determine the effect of nitrogen on vegetative growth, fruit set, fruit weight, yield, maturation index and leaf N,P, and K concentrations. The results indicate that, under these conditions, application of up to 4 kg NH4NO3/tree significantly increased yield to 62.5 kg/tree compared to the control. The positive effect was attributed to the initial and final fruit set increases (7.63 and 3.73%, respectively). However, the weight of 100 olives (337 g) at the same fertilizer rate was considerably lower compared to the control. Higher nitrogen rates decreased yield while increasing overall shoot growth. Nitrogen fertilization did not significantly influence the oil content of olive fruit. Fruit weight, maturation index and concentration of oil reached maximum levels in the beginning of December, indicating a suitable start to olive harvesting with reduced yield losses due to unfavorable weather conditions. With increasing fertilizer levels from 0 to 6 kg NH4NO3,/tree concentration of N in olive leaves also increased from 1.23% to 2.38%. Maximum yield was achieved at a level of 6 kg NH4NO3/tree, which corresponded to 2.01% N in leaves. The results suggest that application of 3 kg NH4NO3/tree can be recommended for table olive pro-duction due to the fact that fruit weight was not decreased, while fertilization with 4 kg NH4NO3/tree was suitable for oil olives.

Soil sodicity is a major constraint to seedling emergence and crop production, potentially reducing plant growth due to physical and chemical constraints. Examining responses to ion imbalances may assist in the identification of genotypes tolerant to chemical constraints in sodic soils and improve productivity. We evaluated the performance of four wheat (Triticum aestivum. L) genotypes in solutions with five sodium adsorption ratios (SARs) ranging from 0 to 60. For all four genotypes, seedling emergence and shoot dry matter (DM) decreased significantly with increasing SAR. A significant positive correlation was observed between Ca concentration in roots, as well as both root and shoot DM for all genotypes. At SAR values &gt;20, a more tolerant genotype (EGA Gregory), had higher Ca concentrations in root tissues, while a more sensitive genotype (Baxter) exhibited Na-induced Ca deficiency. Thus, selection for genotypes that are able to accumulate Ca in roots in sodic conditions may be a useful trait for selecting genotypes tolerant of soils with high ESP values. However, for soils that restrict plant growth at ESP (SAR) values of 6-10%, it is likely that growth is restricted by physical constraints rather than by a Na-induced Ca deficiency.

Macrophytes and seagrasses play a crucial role with a variety of the functions in the marine ecosystem and respond synchronized-promptly to the changing climate, followed by ecological status. Monitoring of the seagrasses was one of the paramount topics at the marine environment. One of the fast monitoring techniques is the vegetation acoustics with the advantages compared to the other remote sensing techniques. The acoustic method alone is ambitious to sea-truth identities of the backscatterers at the sea. Therefore, a package of computer programs was developed to identify, estimate leaf biometrics (leaf length and biomass) of one of the common seagrasses, Posidonia oceanica. Some troubleshooting in the acoustical data were fixed and then solved to reach the estimates regarding to the matters encountered for the vegetation as well as fisheries and plankton acoustics. One of the trouble was the “Lost” bottom which occurred during the data collection and post-processing due to occurrence of the acoustical noises, reverberations, interferences and intense scatterers suchlike fish schools. Another problem to remove was the occurrence as studying the near-bottom echoes belonging to submerged vegetations such as seagrasses, followed by spurious echoes. Last one was recognition of the seagrass to estimate leaf length and biomass calibrating the sheaths/vertical rhizome of the seagrass and establishing relationships between acoustical units and biometrics, respectively. As a consequence, an autonomous package of the code written in language MatLab was developed to perform all processes, and the name of the package was abbreviated as POSIBIOM, POSIdonia BIOMass. This study presented the algorithms, methodology, acoustics-biometrics relation, and mapping of the biometrics for the first time, and discussed advantages and disadvantages of the package compared to the software purposed for the bottom types, habitat and vegetation acoustic. The future studies were recommended to improve the package.

Kandelia obovata (Ko) and Aegiceras corniculatum (Ac) are common and dominant plant species in mangrove wetlands in South China, and distribute in the similar tidal zones along the coastline. The present study aimed to determine the allelopathic effects of leaf litter leachates (LLLs) from Ko and their purified condensed tannins (PCTs) on the germination and growth of Ac by mangrove microcosms. Replicate pots containing five different levels of LLLs and PCTs were separately prepared and propagules of Ac were placed in each treatment. Both LLLs and PCTs significantly inhibited the germination and growth of Ac, especially in high levels. The final germination rates of roots, stems, and the number of fine roots declined continuously while other growth indicators, including the lengths of fine roots, nutritive roots, the biomasses of roots, stems, leaves, increased firstly and then decreased with increasing levels. These results indicated that LLLs from the leaf litter of Ko, in particular, their PCTs exerted an inhibition effect on propagule germination and seedling growth of Ac, and the inhibitory effects were concentration dependent. This study suggested that condensed tannins from leaf litter, acting as allelochemicals, could regulate the natural regeneration of a mangrove forest.

This study was carried out in three phases. During the first stage, the physicochemical properties of French sauce prepared from different blends of soybean and olive oils were evaluated. In the second stage, the oxidative stability of the optimum sauce sample from the first stage enriched with various amounts of olive leaf polyphenolic extract (OLE) (obtained via ultrasound-assisted extraction) was investigated over 90 days of storage. During the third stage, the microbiological and sensory properties of the samples containing the optimum amounts of OLE, as a substitution for the synthetic preservatives, were studied. According to the results, addition of olive oil at higher levels (75 and 100%) could affect the physicochemical properties of the sauce as compared to the control sample. It was also found that the addition of olive oil (up to 50%) would not significantly impact the sauce properties. Regarding the OLE enrichment in the samples, it was found that high levels of OLE could improve the oxidative stability of the samples. Based on the results of the experiments in the third phase, it was found that OLE could be used as a preservative instead of the commercial ones. Overall, this study suggests the potential use of olive oil and olive leaf extract in the preparation of French sauce to boost its nutritional value and its stability.

Rising temperature is among the most remarkably stressful phenomena induced by global climate changes with negative impacts on crop productivity and quality. It has been previously shown that volatiles belonging to the isoprenoid family can confer protection against abiotic stresses. In this work, two Vitis vinifera cv. ‘Chardonnay’ clones (SMA130 and INRA809) differing for a mutation of the DXS gene encoding for 1-deoxy-D-xylulose-5-phosphate (the first dedicated enzyme of the 2C-methyl-D-erythritol-4-phosphate (MEP) pathway) and involved in the regulation of isoprenoids biosynthesis were investigated in field trials and laboratory experiments. Leaf monoterpene emission, chlorophyll fluorescence and gas-exchange measurements were assessed over three seasons at different phenological stages and either carried out in vivo or controlled conditions under contrasting temperatures. A significant (p<0.001) increase in leaf monoterpene emission was observed in INRA809 when plants were experiencing high temperatures and over two experiments while no differences were recorded for SMA130. Significant variation was observed for the rate of leaf CO2 assimilation under heat stress, with INRA809 maintaining higher photosynthetic rates and stomatal conductance values than SMA130 (p=0.003) when leaf temperature increased above 30°C. At the same time, maximum photochemical quantum yield of PSII (Fv/Fm) was affected by heat stress in the non-emitting clone (SMA130), while the INRA809 showed a significant resilience of PSII under elevated temperature conditions. Consistent data were recorded between field seasons and temperature treatments in controlled environment conditions suggesting a strong influence of monoterpene emission on heat tolerance under elevated temperatures. This work provides further insights on the photoprotective role of isoprenoids under high temperatures in Vitis vinifera and additional studies should focus at unravelling the mechanisms underlying heat tolerance on the monoterpene-emitter grapevine clone.

Actinidia arguta (Sieb. et Zucc.) Planch. ex Miq. is abundant of vitamin C and bioactive compounds with high antioxidant activities. In this study, eight wild A. arguta accessions from different areas in Northeast China were collected. Some bioactive compounds were examined on the different tissues of different germplasms including four kinds of leaves, petioles and fruits. The method of UPLC-MS was used to detect the flavonoid compounds. The results showed that some bioactive compounds including vitamin C, soluble sugar, free amino acid, total phenolics and flavonoids content showed significant differences between six tissues of A. arguta accessions and showed significant variability with maturity. In eight accessions, the highest vitamin C content was found in young apical leaves of ‘CBS-6’ (7.47 mg/g fresh weight), and the highest soluble sugar content was in fruits of ‘CJ-1’ (196.52 mg/g fresh weight) and the highest total phenolic content and total flavonoids content were in young apical leaves of ‘CBS-11’ (3.48 mg/g fresh weight) and of ‘CBS-3’ (2.00 mg/g fresh weight), respectively. Ten flavonoid compounds including kaempferol, isorhamnetin and quercetin were detected in leaves, petioles and fruits. The total content of flavonoids were highest in young apical leaves (10219.84 µg·g-1) and the lowest in fruits (78.75 µg·g-1). Based on the comparison of the contents of several bioactive compounds, the two accessions ‘CJ-1’ and ‘CBS-8’ had relatively outstanding performance, and in the comprehensive evaluation of the antioxidant activity among different tissues, the young leaves had the strongest antioxidant activity. These results highlighted the antioxidant potentialities of A. arguta leaves as a major source of phenolics and vitamin C as well as flavonoids. It provided a theoretical basis for the utilization of leaves of A. arguta.

Recent climatic changes have resulted in an increased frequency and prolonged periods of drought and strained water resources affecting plant production. We explored the possibility of reducing irrigation in a container nursery and studied the growth response of seedlings of economically important forest trees: broadleaf deciduous angiosperms Fagus sylvatica, Quercus petraea and evergreen conifers Abies alba and Pinus sylvestris. We also studied markers of water stress including modifications of biomass allocation, leaf anatomy, proline accumulation and expression of selected genes. Growth of the broadleaved deciduous species was more sensitive to the reduced water supply than that of conifers. Remarkably, growth of the shade tolerant Abies was not affected. Adjustment of biomass allocations was strongest in P. sylvestris, with a remarkable increase in allocation to roots. In response to water deficit both deciduous species accumulated proline in leaves and produced leaves with shorter palisade cells, reduced vascular tissues and smaller conduit diameters, but not conifers. Relative transcript abundance of a gene encoding a Zn-finger protein in Q. petraea and a gene encoding a pore calcium channel protein 1 in A. alba increased as water deficit increased. These findings suggest that in container nursery, the genetic selection can be initiated by water deficit. Our study shows major differences between functional groups in response to irrigation, with seedlings of evergreen conifers having higher tolerance than the deciduous species. This suggests that major water savings could be achieved by adjusting irrigation regime to functional group or species requirements.

This paper studies the use of zinc oxide nanoparticles (ZnO-NPs) synthesized using an extract of convolvulus leaves and expired ZnCl₂, as an efficient inhibitor for carbon steel corrosion in 1M HCl solution. ZnO-NPs are characterized by Fourier-transform infrared spectrophotometer (FTIR) and UV–Vis analysis. The technique of weight loss, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS) has also been used to investigate the prevention of carbon steel corrosion in 1M HCl. The results showed that the efficiency of restraint increased when the concentration of ZnO-NPs was raised to 91% and that the inhibition efficiency is still high despite its decrease at high temperature, and it acts as a mixed-type inhibitor A sample of carbon steel with the protective inhibitor layer on top was immersed for 20 hours and observed; an increase in the charge transfer resistance (Rct) and stability of the inhibitor was noticed after 6 hours. Adsorption isotherm models demonstrated that the inhibitor adsorption mechanism on the carbon steel surface followed Langmuir, more than Freundlich and Temkin, behavior. The thermodynamic parameters showed that the adsorption process is a mixed adsorption, spontaneous, and exothermic. The results illustrated that the acid medium was a strong inhibitor of carbon steel corrosion. Scanning electron microscope (SEM) showed that the ZnO-NPs formed a good protective film on the carbon steel surface.

Sugarcane is the primary crop in the global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution is required to solve the issues given by manual identification of plant diseases, which is time-consuming and wasteful, as well as low detection accuracy. This paper proposes the development of a robust deep ensemble convolutional neural network (DECNN) model for the accurate detection of sugarcane leaf diseases. Initially, several transfer learning (TL) models, including EfficientNetB0, MobileNetV2, DenseNet121, NASNetMobile, and EfficientNetV2B0, were enhanced through the addition of specific layers. A comparative study was then conducted on the improved dataset. The application of data augmentation, along with the addition of dense layers, batchnormalization layers, and dropout layers, led to improved detection accuracy, precision, recall, and F1 score for each model. Among the five enhanced transfer learning models, the modified EfficientNetB0 model demonstrated the highest detection accuracy, ranging from 97.08% to 98.54%. In conclusion, the DECNN model was developed by integrating the modified EfficientNetB0, MobileNetV2, and DenseNet121 models using a distinctive performance-based custom weighted ensemble method, with weight optimization carried out using the Tree-structured Parzen Estimator (TPE) technique. This resulted in a model that achieved a detection accuracy of 99.17%, outperforming the individual performances of the modified EfficientNetB0, MobileNetV2, and DenseNet121 models in detecting sugarcane leaf diseases.

Green leaf volatiles (GLV) are important signaling compounds that help to regulate plant defenses against pests and pathogens. Made through the hydroperoxide lyase (HPL) pathway, they are rapidly produced upon damage and can signal this to other parts of the same plant or even plants nearby, where they can induce rapid defense responses directly or prime them against the impending danger. In this primed state plants can respond faster and/or stronger should pests or pathogens attack. However, while all proteins and genes involved in the biosynthesis of GLV have been identified, little is still known about how the first two steps in the pathway, e.g., oxygenation by a lipoxygenase (LOX) and subsequent cleavage by HPL are facilitated within the damaged tissue, resulting in the production of Z-3-hexenal (Z3al) as the first committed product of the pathway. Here, we provide evidence that several factors might be involved in the production of Z3al including pH, Ca2+, and an environment that is highly hydrophobic. We present a model in which the extraordinary circumstances that are present at the site of Z3al production are considered and shine new light on potential regulatory mechanisms.

Polar auxin transport (PAT) and auxin efflux carriers are known components controlling leaf complexity and venation patterns in some model plant species. Evidence indicates that PAT generates auxin converge points (CPs) that in turn lead to local leaf formation and internally into major vein formation. However, the role of PAT in more diverse leaf arrangements and vein patterns is largely unknown. We used pharmacological inhibition of PAT in developing pinnate tomato, trifoliate clover, palmate lupin, and bipinnate carrot leaves and observed dosage-dependent reduction to simple leaves in these eudicots. Leaf venation patterns changed from craspedodromous (clover, carrot), semi-craspedodromous (tomato) and brochidodromous (lupin) to more parallel patterning with PAT inhibition. The visualization of auxin responses in transgenic tomato plants showed that discrete and separate CPs in control plants were replaced by diffuse convergence areas near the margin. These effects indicate that PAT plays a universal role in the formation of different leaf and vein patterns in eudicot species via a mechanism that depends on the generation as well as separation of auxin CPs. Computer simulations indicate that variations in PAT can alter the number of CPs, corresponding leaf lobe formation and position of major leaf veins along the leaf margin in support of experimental results.

In order to understand the defense response of Sanchezia nobilis to biotic and abiotic stresses, this study investigated the phenomenon of S. nobilis under biotic of feeding by Achatina fulica, and measured the temporal changes of malondialdehyde content, catalase and peroxidase activities under biotic of feeding by Achatina fulica and abiotic stress of garden pruning conditions on S. nobilis. The results showed that: 1) S. nobilis was seriously damaged by A. fulica and the average hourly food intake of A. fulica is 0.22 ± 0.01 g. 2) Leaf cutting stimulation and snail feeding stress significantly affected the content of malondialdehyde, the activities of catalase and peroxidase in the leaves of S. nobilis. 3) Compared with the control group, the snail feeding and leaf cutting stimulated stress could induce the leaf of S. nobilis to synthesize more malondialdehyde under varying durations, and the activities of catalase and peroxidase were significantly enhanced in a short time, and the induction response of snail feeding biological stress was faster than that of leaf cutting stimulated abiotic stress. 4) After the defense enzyme was strengthened, the activities of catalase and peroxidase in the feeding stress group of snails showed a gradual downward trend, while the activities of catalase and peroxidase in the leaf cutting stimulation stress group showed a first upward and then downward trend, indicating that abiotic stress had more lasting effects on plants than biological stress. It can be seen that biotic stress and abiotic stress significantly affect the defense response of plants, and the defense-related substances malondialdehyde, catalase and peroxidase play an important role in plants' response to biotic and abiotic stress. This study has important production guidance significance for carrying out stress resistance breeding or cultivation management of garden plants.

Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. Assessment of plant growth and composition is vital for determining crop yield and overall quality, however, classical laboratory analyses are slow and costly. Therefore, new, less expensive, more rapid, and non-destructive approaches are being developed, including those based on (hyper)spectral reflectance. Additionally, it is also important to determine how plant phenotypes respond to fertilizer treatments and whether these differences in response can be detected from analyses of hyperspectral image data. In the current study, we demonstrate the suitability of hyperspectral imaging in combination with machine learning models to estimate the content of chlorophyll (SPAD), anthocyanins (ACI), glucose, fructose, sucrose, vitamin C, β-carotene, N, P, K, dry matter content, and plant fresh weight. The implemented five classification and regression machine learning models showed high accuracy in classifying the lettuces by the applied fertilizers treatments and estimating nutrient concentrations. To reduce the input (predictor data, i.e., hyperspectral data) dimension, 13 principal components were found and applied in models. The implemented artificial neural network models of the machine learning algorithm demonstrated high accuracy (r = 0.85 ... 0.99) in estimating fresh leaf weight, and contents of chlorophyll, anthocyanins, N, P, K, and β-carotene. The four applied classification models of machine learning demonstrated 100% accuracy in classifying the studied baby leaf lettuces by phenotype when certain fertilizer treatments were applied.

Alpinia oxyphylla is a traditional Chinese medicinal plant, with a medicinal history of more than 1,700 years. Ring leaf blight (RLB) disease, caused by pestalotioid species, is an important disease of A. oxyphylla, seriously affecting the yield and quality of fruits. The causal agent of RLB disease has not been systematically identified and characterized so far. In this study, thirty-six pestalotioid strains were isolated from the leaves and stems of A. oxyphylla collected from 6 cities of Hainan province, China. Based on multi-locus phylogeny (ITS, tef-1α and tub2) and morphological characteristics analyses, seventeen species belonging to three genera (Neopestalotiopsis, Pestalotiopsis and Pseudopestalotiopsis) were identified and six new species (N. baotingensis, N. oblatespora, N. olivaceous, N. oxyphylla, N. wuzhishanensis and N. yongxunensis) were described. Pathogenicity tests revealed that strains of Neopestalotiopsis species caused more severe ring leaf blight on A. oxyphylla than strains of Pestalotiopsis and Pseudopestalotiopsis under wounded inoculation conditions.

Gamba grass (Andropogon gayanus Kunth) is a tussock-forming forage species adapted to acid soils of Brazilian savannas and cultivated for grazing pastures. Four decades since its releasing, Planaltina prevails as the most commercialized cultivar of the species, even though the new cultivar BRS Sarandi could be a better alternative for Gamba grass-based farms by presenting greater leaf:stem ratio. The objective of this study was to evaluate the average daily live weight gain (ADG) of Nellore bulls (Bos indicus) for two Gamba grass cultivars – Planaltina and Sarandi. The experiment was conducted in Planaltina, Federal District, Brazil, during 3 years, namely 2018, 2018-2019 and 2020. The experimental design was completely randomized block with two treatments and three replicates, each one continuously stocked at three stocking rates (SR) - 1.3, 2.6 and 4 young bulls/ha. Canopy height (CH), forage mass (FM), plant-part proportion (leaf, stem and dead material) and nutritive value were evaluated. In 2018, mean ADG for Sarandi pastures was greater (0.690 kg/bull/d) than Planaltina (0.490 kg/bull/d) (P=0.0839). In the subsequent year (2018-2019), there was no effect of cultivar (P = 0.4089), while in 2020 the ADG was again affected by cultivar (P=0.0412), confirming the advantage of Sarandi (0.790 kg/bull/d) over Planaltina (0.650 kg/bull/d). In 2018 and 2020, the percentage of stems for Sarandi was about 3-6 pp lesser than for Planaltina (P

Green leaf volatiles (GLVs) are important in giving grape a fresh and green aroma. But the changes in GLVs during the phenological development of grapevines are not well known. This study analysed the GLVs and transcription levels of associated biosynthetic genes in six grape species from the Loess Plateau region at five stages of maturation. Thirteen GLVs were detected, showing unique patterns for each grape type at various growth phases. The primary components in six grapes were (E)-2-Hexenal, (E)-2-Hexen-1-ol, and Hexanal. With the exception of Cabernet Franc in 2019, the overall GLV contents of the six types generally increased during growth and development, peaking or stabilizing at harvest. And Sauvignon Blanc, Cabernet Gernischt, and Cabernet Sauvignon exhibited higher total contents among the varieties. Correlation analysis revealed a strong positive correlation between the levels of Hexanal, 1-Hexanol, (E)-2-Hexen-1-ol, (Z)-3-Hexenyl acetate, Nonanal, and (E, E)-2,6-Nonadienal and the expression of VvHPL and VvAAT genes in the LOX-HPL pathway. Specifically, VvHPL emerges as a potential candidate gene responsible for species-specific differences in GLV compounds. Comprehending the changing patterns in the biosynthesis and accumulation of GLVs offers viticulturists and enologists the opportunity to devise targeted strategies for improving the aromatic profile of grapes and wines.

The diversity of leaf characteristics, including leaf color, is a prominent subject of study in plant science. Leaf color is predominantly determined by the synthesis and functionality of chlorophyll, a key component of photosynthesis. The regulation of chlorophyll synthesis and degradation involves complex gene interactions, and disruptions in these processes can lead to abnormal chlorophyll synthesis and impact leaf color. This study focuses on Bambusa multiplex f. silverstripe, a natural variant with various leaf colors, including green, white, and green-white leaves. The variations in leaf color are attributed to genetic factors and their influence on gene expression. By employing RNA-seq, we investigate the molecular mechanisms behind chlorophyll anomalies and genetic factors in Silverstripe. Our findings shed light on the complexity of gene interactions and regulatory networks that underlie leaf color diversity and provide valuable insights for future research and plant breeding.

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural network (CNN) and LSTM to capture spatial-temporal patterns from the data and improve the model ability to recognize dynamic features of the disease. In addition, we introduced an attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epochs) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the incidence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers made accurate management decisions and reduced disease impacts through optimal fungicide application timing.

In large-scale parallel computing and communication systems, an interconnect network structure is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. In a graph, the vertices and edges are likely to fail, so we must think about the fault tolerance of a graph. Connectivity is an important parameter in the study of faulty tolerance for a graph. In this paper, we study a special class of connectivity: $m$-component connectivity, this is a natural generalization of the classical connectivity of graphs defined in terms of the minimum vertex-cut. Let $F$ is a vertex set of $G$ $(i.e, F\subseteq V(G))$, if the following conditions are satisfied, we say $F$ is a $m$-component cut: $(1)$ $G-F$ is disconnected; $(2)$ the number of components in $G-F$ is greater than or equal to $m$. In another word, the $m$-component connectivity $c\kappa_{m}(G)$ is defined as $min\{|F|~|~F\subseteq V(G)$ and $F$ is a $m$-component cut$\}$. Determining $m$-component connectivity is still unsolved in most interconnection networks even for small $m'$s. Leaf-sort graph is a special Cayley graph, it has many special properties that are different from other Cayley graphs. So we need to pay attention to some of it's special properties when we study it. In this paper, we can get the values: $c\kappa_{3}(CF_{n})=3n-6$ $(n$ is odd$)$ and $c\kappa_{3}(CF_{n})=3n-7$ $(n$ is even$)$ for $n\geq3$; $c\kappa_{4}(CF_{n})=\frac{9n-21}{2}$ $(n$ is odd$)$ and $c\kappa_{4}(CF_{n})=\frac{9n-24}{2}$ $(n$ is even$)$ for $n\geq4$; $c\kappa_{5}(CF_{n})=6n-16$ $(n$ is odd$)$ and $c\kappa_{5}(CF_{n})=6n-18$ $(n$ is even$)$ for $n\geq5$.

Canistel (Pouteria campechiana (Kunth) Baehni) (yellow sapote, canistel). Originally from Mexico, the fruit is edible, and the tree is used as an ornamental and medicinal plant. Descriptive studies were carried out with the objective of morphologically characterizing the fruits and leaves of trees located in different regions of Mexico to select outstanding specimens and propose their massive reproduction. The trees were selected in three zones. In zone 2 from flower anthesis to harvest 11 months, fruits with 3 seeds, greater weight (180 to 330 g) and quantity of pulp (198.88 g), subglobose shape, total soluble solids 33 %. The fruits of zone 1 at harvest 8 months and zone 3 took 9 months to be harvested, in the rest of the characteristics evaluated were exceeded by the fruits of zone 2. To characterize the canistel (P. campechiana) leaf and identify accessions or cultivars, the three main variables, according to the principal component analysis were: leaf area, perimeter, and minor diameter. The three zones where canistel fruits and leaves were characterized are important and can be recommended for propagation in zones with favorable climatic conditions for fruiting in tropical and subtropical regions of Mexico.

Although jojoba (Simmondsia chinensis) has been cultivated for years, information on its N re-quirements is limited. A 6-year study of mature jojoba plants grown under field conditions with an intensive management regime evaluated the effect of N-application rate on plant nutrient status, growth and productivity, and nitrate accumulation in the soil. Five levels of N application were tested: 50, 150, 250, 370 and 500 kg N ha-1. The fertilizers were provided all along the growing season via subsurface drip irrigation system. Leaf N concentration, in both spring and summer, reflected the level of N applied. Diagnostic leaf (youngest leaf that has reached full size) concentration of 1.3% N was identified as the threshold for N deficiency. Increasing rates of N application resulted in higher P levels in young leaves. Plant K status, as reflected by leaf analysis, was not affected by N treatment but was strongly affected by fruit load. Vegetative growth was inhibited when only 50 kg N ha-1 was applied. Soil analysis at the end of the fertilization season showed substantial accumulation of nitrate for the two highest application rates. Considering productivity, N costs, and environmental risk, 150 kg N ha-1 is the recommended dosage for in-tensively grown jojoba. N deficiencies can be identified using leaf analysis and excess N can be detected by soil sampling toward the end of the growing season. These results and tools will fa-cilitate precise N fertilization in intensive jojoba plantations.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.

The effect of the addition of fenugreek seed powder and Moringa oleifera leaf meal on the production performance and egg quality of Lohmann Brown laying hens was investigated. A total of 108 Lohmann-Brown layers, 48 weeks of age, were used and randomly assigned to six experimental groups of three replicates each in a randomized design. A control diet was supplemented with 3.6 g/hen/day of Moringa oleifera leaf meal and fenugreek seed powder alone or in various proportions as follows: control diet (T1), 100% Moringa oleifera leaf meal (T2), 25% fenugreek seed powder and 75% moringa oleifera leaf meal (T3), 50% fenugreek seed powder and 50% moringa oleifera leaf meal (T4), 75% fenugreek seed powder and 25% moringa oleifera leaf meal (T5) and 100% fenugreek seed powder (T6) were added on top of the control diet. Data were analyzed using one-way ANOVA by the general linear model method, using Tukey's multiple range tests to differentiate means. According to the results, fenugreek and moringa had a crude protein content of 25.17 and 27.8%, respectively. The groups administered T4 had significantly higher mean feed intake (120.75), feed conversion rate (2.315), egg production (77.22), and egg mass (52.08) than the others. Treatment 4 had a significant (P&lt;0.05) improvement in some egg quality traits such as shell weight, shell firmness, albumen weight, yolk weight, yolk color, and Haugh unit compared to the control. In summary, Treatment 4, a mixture of equal parts fenugreek seed powder and Moringa oleifera leaf meal, improved egg production performance and egg quality.

leaf beetle (BLB) (Ootheca mutabilis) has emerged as an important bean pest in Uganda, leading to devastating crop losses. There is limited information on the population genetic structure of BLB despite their importance. In this study, novel microsatellite markers and the partial mitochondrial cytochrome oxidase subunit I (mtCOI) gene sequences were used to analyze the spatial population genetic structure, genetic differentiation, gene flow and haplotype diversity of 87 O. mutabilis samples from five populations. We identified 19,356 simple sequence repeats (SSRs) (mono, di, tri, tetra, penta, and hexa-nucleotides) of which 81 di, tri and tetra-nucleotides were selected for primer synthesis. Five highly polymorphic SSR markers (4-21 alleles, heterozygosi-ty 0.59-0.84, polymorphic information content (PIC) 50.13-83.14%) were used for this study. Analyses of the five O. mutabilis populations with these five novel SSRs found 89% of genetic variation occurring within individuals, 9% among individuals and 2% among populations. Genetic differentiation was low but significant for SSR and insignificant for mtCOI partial sequence data while gene flow was high across the populations. There was no evidence of isolation by distance between geographical and genetic distances. Bayesian clustering identified signature of admixture that suggests genetic contributions from two ancestral genetic lineages, and the median-joining haplotype network showed low differentiation of many different haplotypes from the most common haplotype. Low genetic differentiation and high gene flow indicates unrestricted migrations between populations. This information will contribute to the design of BLB control strategies.

Some biostimulants, including plant origin preparations, act similarly to plant hormones. Moreover, the supplementation of known and unknown rooting cofactors can stimulate rhizogenesis in cuttings. The aim of this research was to assess the response of difficult-to-root and long-rooting stem cuttings of the once-blooming old variety Rosa ‘Hurdal’ to preparations of plant origin. The hypothesis was that plant origin preparations could enhance rooting processes by inhibiting chlorophyll a/b degradation in leaves and postponing leaf senescence, simultaneously increasing the quality of cuttings. The one-bud stem cuttings were made in four phenological stages: (H1) flower buds closed, (H2) open flowers, (H3) just after petal fall, (H4) 7-14 days after petal shedding. They were treated with either standard commercial powder preparations containing 0.4% indolebutyric acid (IBA) or 0.2% naphthalene acetic acid (NAA) as well as with commercial plant origin preparations that this work will henceforth refer to as: Algae Extract, Organic Preparation, and Plant Extract. The cuttings were evaluated after 12 weeks of rooting them in two substrates: peat-perlite and peat-sand (v:v; 1:1). Mean root percentages for both substrates were noted after preparation from stage H1 (74.5 %), H2 (59.5 %), H3 (50.8 %) shoots. The H4 cuttings didn’t root at all and were not considered further. The means for all phenology stages together were the highest by the use of 0.6 % Algae Extract, 0.012 % and 0.02 % Organic Preparation, 0.2 % and 0.4 % Plant Extract. The lowest means were reported for the control cuttings as well as NAA and IBA treatment. Plant origin preparations encouraged growth parameters but did not unequivocally inhibit the decrease of chlorophyll content in the cuttings’ leaves. Rooting percentage depended on the quality of cuttings as well as chlorophyll a/b and soluble protein content in leaves in both rooting substrates.

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Our simulation framework could contribute to better understand performances for species discrimination and relationship between spectral variations and taxonomic and functional dimensions of biodiversity.

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterization in plant leaves utilizing the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterization kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5\% to 22\%, and increased from 83.12\% to 99.33\% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

Oleuropein (Ole), a secoiridoid glucoside present in Olea europaea leaves, gained the interest of many scientists thanks to its several biological properties, including the anticancer one. We verified whether Ole might potentiate cytotoxicity of conventional drugs used to treat melanoma, disclosing new potential therapeutic strategy. We tested the cytotoxic action of Ole alone or in combination with chemotherapeutics on A375 human melanoma cells. We found that Ole was able, at a dose of 500 μM, to stimulate apoptosis in melanoma cells, while at a non-toxic dose of 250 μM, it affected cell proliferation and induced the downregulation of pAKT/pS6 pathway. 250 μM Ole did not potentiate the effect of Vemurafenib (PLX4032), but it succeeded in increase the cytotoxic effect of Dacarbazine (DTIC). The mayor effect was found in the association between Ole and Everolimus (RAD001), also on PLX4032-resistant BRAF melanoma cells, possibly cooperating in the inhibition of pAKT/pS6 pathway. Of interest, an olive leaf extract enriched in equimolar Ole was more effective and able to further improve DTIC and, particularly, RAD001 efficacy on BRAF melanoma cells than Ole alone. Therefore, Ole represents a natural product able to potentiate a wide array of chemotherapeutics against BRAF melanoma cells affecting pAKT/pS6 pathway.

The barley (Hordeum vulgare) genome comprises over 32,000 genes, with differentiated cells expressing only a subset of genes; the remainder being silent. Mechanisms by which tissue-specific genes are regulated are not entirely understood, although DNA methylation is likely to be involved. DNA methylation patterns are not static during plant development, but it is still unclear whether different organs possess distinct methylation profiles. Methylation-sensitive GBS was used to generate DNA methylation profiles for roots, leaf-blades and leaf-sheaths from five barley varieties, using seedlings at the three-leaf stage. Differentially Methylated Markers (DMMs) were characterised by pairwise comparisons of roots, leaf-blades and leaf-sheaths of three different ages. While very many DMMs were found between roots and leaf parts, only a few existed between leaf-blades and leaf-sheaths, with differences decreasing with leaf rank. Organ-specific DMMs appeared to target mainly repeat regions, implying that organ differentiation partially relies on the spreading of DNA methylation from repeats to promoters of adjacent genes. Furthermore, the biological functions of differentially methylated genes in the different organs correlated with functional specialisation. Our results indicate that different organs do possess diagnostic methylation profiles and suggest that DNA methylation is important for both tissue development and differentiation and organ function.

Leaf temperature (T_leaf) influences photosynthesis and respiration. Currently, there is a growing interest on including lianas in productivity models due to their increasing abundance, and their detrimental effects on net primary productivity in tropical environments. Therefore, understanding the differences of T_leaf between lianas and trees is important for future of forest on whole ecosystem productivity. Here we determined the displayed leaf temperature (T_d= T_leaf – ambient temperature) of several species of lianas and their host trees during ENSO and non-ENSO years to evaluate if the presence of lianas affects the T_d of their host trees, and if leaves of lianas and their host trees exhibit differences in T_d. Our results suggest that close to midday, the presence of lianas does not affect the T_d of their host trees; however, lianas tend to have higher values of T_d than their hosts across seasons, in both ENSO and non-ENSO years. Although lianas and trees tend to have similar physiological-temperature responses, differences in T_d could lead to significant differences in rates of photosynthesis and respiration based temperature response curves. Future models should thus consider differences in leaf temperature between these life forms to achieve robust predictions of productivity.

The complex network relationship between leaf traits and their multiple traits is closely related to the environment, and is the result of interactions between various environmental factors such as climate and soil. Based on previous work, we selected 20 P. euphratica trees along the desert riparian forest of the main stream of the Tarim River for leaf sample collection and habitat survey. We used 27 leaf traits of P. euphratica to visualize the leaf trait network (LTN). Through network structure parameters, such as edge density, diameter, average path length, and average clustering coefficient, the spatial pattern of the LTN and its relationships with 11 soil factors and 19 climatic factors were discussed using principal component analysis and correlation analysis. The results showed that: (1) there were significant differences in the parameters of the leaf trait network of P. euphratica along the main stream of the Tarim River. The variation coefficients of the diameter and average path length were the largest, respectively, whereas that of the average clustering coefficient was the smallest. (2) There were both positive and negative correlations between each parameter and soil factors, such as soil water content, total potassium, and organic matter, and climatic factors, such as isothermality, minimum temperature of the coldest month, and precipitation of the wettest month. (3) The explanatory power of climate alone on the leaf traits of P. euphratica was generally higher than that of soil factors, indicating that climatic conditions play a more decisive role in shaping the network structure of leaf traits of P. euphratica. However, regarding the influence of network modularity, the contribution of soil factors was significant, indicating that the influence of soil conditions on some LTN parameters cannot be ignored.

Accordingly, a lot of dry direct seeding rice would be dense planted, that the initial seedling quantity could approach or even exceed the targeted effective panicle number. While limit information is available to evaluate the effects of involved novel nitrogen application methods, that total nitrogen once applied at a determined leaf age stage, on the yield formation traits of such dense planted dry direct seeding rice. The two-year experiments with total 180 kg ha-1 nitrogen once applied time treatments at sixth (L6), seventh (L7), eighth (L8), ninth (L9) and tenth (L10) leaf age stages were carried out. Results showed that the above-ground dry matter weight at maturity and grain yield of L8 was 2.1% ~ 11.8% and 4.0% ~ 9.9% higher than those of other treatments. Compared to other treatments, the higher above-ground dry matter weight after heading stage, bigger mean leaf area index with higher leaf area index at heading stage, increased leaf area duration, crop growth rate and net assimilation rate from jointing to heading period were important traits of higher grain yield formation of L8. In addition, the L8 had relative higher grain-leaf ratio traits than other treatments. Overall, a total 180 kg ha-1 nitrogen with mixed urea and compound fertilizer once applied at eighth leaf age stage would be suggest a useful high yield cultivation method for dense planted dry direct seeding rice in rice-wheat rotation system.

(1) Selecting high-quality varieties with disease resistance by crossbreeding artificially is the most fundamental way to address the damage caused by Calonectria spp in eucalypt plantations. However, understanding the mechanism of the formation of disease-resistant heterosis in eucalypt is crucial for the success of crossbreeding. (2) Two eucalypt hybrids, susceptible EC333 (H1522 × unknown) and resistant EC338 (W1667 × P9060) were screened in our previous study on the infection of the isolates of Calonectria., which causes eucalypt leaf blight. RNA-Seq sequencing was performed on susceptible hybrid EC333, disease-resistant hybrid EC338 (W1667 × P9060), and their parents. A comparison was conducted between the the transcriptomic data sequenced and the reference genome of Eucalyptus grandis, and the The differential expression of genes and functional annotations between hybrids and parents was identified. (3) Gene differential expression analysis shows that there are 3912 differentially expressed genes between EC333 and EC338, with 1631 up-regulated and 2281 down-regulated. The expression trends of differential gene sets in P9060 and EC338 were similar. However, the expression trend of W1767 was opposite that of EC338. The similarity of expression levels and the advantage of stress resistance of E. pellita suggested that genes with significant differences in expression are likely to be related to disease resistance. The GSEA enrichment based on GO annotation revealed that the carbohydrate binding pathway (GO:0030246) is differentially expressed between EC338 and EC333. The gene pathway that differentially expressed between EC338 and EC333, was revealed by GSEA enrichment based on KEGG annotation, is Sesquiterpenoid and Triterpenoid biosynthesis (EGR00909). The Alternative Splicing analysis demonstrated that the AS events gene between EC338 and EC333 is LOC104426602. According to SNP analysis, EC338 has 626 more high-impact mutation loci than the male parent P9060, and 396 more than the female parent W1767, the maternal parent W1767 has 259 more mutation loci in the downstream region than EC338, while the male parent P9060 has 3107 fewer mutation loci in the downstream region than EC338. Additionally, EC338 has 9631 more mutation loci in the exon region than EC333. These findings suggested that the genetic pathways, specific gene regions, and their quantities might play crucial roles in the formation of heterosis linked to the resistance against eucalypt leaf blight. The modules were found in WGCNA, which were strongly and oppositely correlated with EC338 and EC333, such as module MEsaddlebrown is likely to be associated with leaf blight resistance. (4) The present study provides a detailed explanation of the genetic basis for the heterosis of resistance to eucalypt leaf blight, providing a basis for exploring genes related to resistance to eucalypt leaf blight.

Rice has become the second major food among rural people. It is one of the most nutrient and low-cost food available in Asia. Rice is composed of two major subspecies as Japonica and Indica and it belongs to the Poaceae family. The World Bank predicts that rice demand will grow 51% faster than population growth in 2025. It is high time to develop a model which predicts the type of infected paddy leaf and avoids the loss of grains. In this paper, the proposed hybrid CNN model, three CNN networks with different layer properties are trained parallel. The extracted feature from these CNN networks is combined to provide the classification of the type of infected leaf. Fieldwork was carried and about 340 samples are collected from five different types of classes. The proposed hybrid model was tested with both the fieldwork dataset and also with an available dataset on the internet. The experimental results show that the proposed model outperforms the other CNN network and yields an accuracy value of 98%, a precision value of 95%, and a misclassification rate of 4% respectively.

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed Adown down sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the complete intersection over union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieves an mAP50 of 94.3% and an mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3% respectively compared to the original model. The model size is reduced by 0.53 MB to just 5.45 MB, and the number of floating-point operations is reduced by 0.3 G to only 7.8 G. The improved model meets the deployment requirements of mobile embedded devices and can provide a theoretical reference for the automated spraying operations of mulberry leaves.

Let BT4l denotes a subclass of bounded turning functions connected with a four-leaf-type domain. The goal of the study is to probe into coefficients of |b6|, |b7|, |b8|, the bounds of the logarithmic coefficients and the third-order determinants of H3,1, H3,2, H3,3 for functions in this class.

Leaf springs are suspension components constituted of several structural and assembly components. Once the leaves are assembled, a stress state due to the assembly process is developed. Thus, the quantification of the assembly stress level should be known in order to be accounted for in the structural design. The aim of this article is precisely to quantify the assembly stress level in parabolic leaf springs for railway freight wagons. An experimental method with the aid of extensometry is considered to evaluate elastic assembly stresses. Next, a numerical model of the leaf spring assembly process with the required boundary conditions is developed to support the experimental results and allow obtaining a distribution of surface stresses along the master leaf length. The development of this work resulted in a surface stress profile along the length of the master leaf generated by the assembly process, where the deviations of the numerical outcomes regarding the analysed spots with strain gauges are mostly of little relevance.

Stipa tenacissima (S. tenacissima), or esparto, is a perennial tussock grass that coexists with Pinus halepensis (P. halepensis) in semi-arid mediterranean woodlands. This research was carried out to explore diurnal transpiration at leaf level in esparto grass under different levels of pine-esparto competition and in contrasting environmental conditions of soil water. The measurements were spanned from the summer 2020 to spring 2021. The relationship between transpiration and competence was conducted in open and closed P. halepensis stands and taken into account the type of leave (green, senescent) and the maturity of the esparto grass. We observed a higher control of transpiration in green leaves, and correlations between transpiration and soil water were noted exclusively in this type of leaf. Our results substantiated a significant impact of pine competitors on transpiration of esparto grass, particularly during seasons characterized by high water-demand scenarios: the summer drought period and the commencement of the growing and flowering period (spring). Furthermore, our findings confirmed a greater response to transpiration in mature bushes compared to young ones under severe water stress, denoting higher adaptation to drought as esparto ages. Our results may have important implications for the dynamics and management of these semi-arid mixed woodlands, as well as the planning of reforestation programs aimed at restoring esparto grass steppes.

The orchid genus Brachystele Schltr. comprises 20 species distributed from Mexico to Argentina; 10 are found in Brazil. Anatomical studies of Orchidoideae Lindl. have been scarce, and the anatomy and histochemistry of Brachystele are still largely unknown. We characterized the vegetative organs of B. guayanensis (Lindl.) Schltr. using standard anatomical and histochemical microtechniques. Brachystele guayanensis was observed to display the anatomical characters commonly found in the vegetative organs of representatives of the Orchidaceae and Orchidoideae (including a uniseriate epidermis, thin cuticle, amphistomatic leaves, anomocytic, diacytic and tetracytic stomata, a homogeneous mesophyll, collateral vascular bundles, rhizomes with pericyclic fibers, roots with velamen, uniseriate exodermis, endodermis and pericycle). Histochemical tests confirmed the presence of lignin, proteins, and alkaloids, the lipidic nature of the cuticle, starch grains stored in spiranthosomes in the roots, and the composition of the raphides. Alkaloids were observed in great abundance, especially in the roots, and may have potentially useful medicinal activities, as has been observed in groups phylogenetically related to Brachystele.

Porcine epidemic diarrhea virus (PEDV) causes severe enteric disease in pigs, particularly neonatal piglets. Attempts to develop a safe and effective vaccine have been unsuccessful. Ginseng stem-leaf saponins (GSLS), a promising oral adjuvant candidate, can improve intestinal immune responses in poultry and mice. However, the low stability limits the further use. Poly lactic-co-glycolic acid (PLGA), a biocompatible and biodegradable nanoparticle, have been widely used in biomedicine for stable and targeted drug delivery. In this study, we developed GSLS-PLGA nanoparticles (GSLS-NPs) and evaluated the mucosal adjuvant efficacy in vitro and in vivo. GSLS-NPs significantly enhanced antigen internalization and pro-inflammatory cytokine secretion by DC2.4 cells. Mice orally administered GSLS-NPs before intramuscular inoculation generated CD11b+CD8α- and CD11b-CD103+ dendritic cells in the spleen and draining mesenteric lymph nodes, respectively, which are the types mainly responsible for antigen presentation. Additionally, enhanced neutralizing and non-neutralizing antibody responses and expanded activities of specific effector and memory CD4+ and CD8+ T cells were also observed in mice immunized with PEDV vaccines plus GSLS-NPs compared to mice receiving the vaccines alone. Furthermore, GSLS-NPs showed a good safety profile and presented great advantages over GSLS aqueous solution. Collectively, our results highlight the potential of GSLS-NPs as a mucosal adjuvant and provide an attractive vaccination strategy for combatting PEDV. Further study is required to evaluate the efficacy of this mucosal adjuvant in swine.

Analysis of 1382 measures of battery State of Health (SoH) from 283 Nissan Leafs (“Leaf/s”), manufactured between 2011 and 2017, has detected a faster rate of decline in this measure of energy-holding capacity for 30 kWh variants. At two years of age, the mean rate of decline of SoH of 30 kWh Leafs was 9.9% per annum (95% uncertainty interval of 8.7% to 11.1%; n = 82). This was around three times the rate of decline of 24 kWh Leafs which at two years averaged 3.1% per annum (95% uncertainty interval of 2.9% to 3.3%; n = 201). For both variants there was evidence for an increasing rate of decline as they aged, although this was much more pronounced in the 30 kWh Leafs. Higher use of rapid DC charging was associated with a small decrease in SoH. Additionally, while 24 kWh cars with greater distances travelled showed a higher SoH, in 30 kWh cars there was a reduction in SoH observed in cars that had travelled further. The 30 kWh Leafs sourced from United Kingdom showed slower initial decline than those from Japan, but the rate of decline was similar at two years of age. Improvements in the battery health diagnostics, continuous monitoring of battery temperatures and state of charge, and verification of a fundamental model of battery health are needed before causes and remedies for the observed decline can be pinpointed. If the high rate of decline in battery capacity that we observed in the first 2.3 years of a 30 kWh Leaf’s lifetime were to continue, the financial and environmental benefits of this model may be significantly eroded. Despite 30 kWh Leafs accounting for only 14% of all light battery electric vehicles registered for use on New Zealand roads at the end of February 2018, there is also the potential for the relatively poor performance of this specific model to undermine electric vehicle uptake more generally unless remedies can be found.

In the event of an abrupt sunlight reduction scenario there is a time window that occurs between when food stores would likely run out for many countries (~6 months or less) and ~1 year when resilient foods are scaled up. A promising temporary resilient food is leaf protein concentrate (LPC). Although it is possible to extract LPC from tree biomass (e.g. leaves and needles), neither the yields nor the toxicity of the protein concentrates for humans from the most common tree species has been widely investigated. To help fill this knowledge gap, this study uses high-resolution mass spectrometry and an open source toolchain for non-targeted screening of toxins on five common North American coniferous species: Western Cedar, Douglas Fir, Ponderosa Pine, Western Hemlock, and Lodgepole Pine. The yields for LPC extraction from the conifers ranged from 1% to 7.5%. The toxicity screenings confirm that these trees may contain toxins that can be consumed in small amounts and additional studies including measuring the quantity of each toxin are needed. The results indicate that LPC is a promising candidate to be used as resilient food, but future work is needed before LPCs from conifers can be used as a wide-scale human food.

In many developing countries human health problems are solved using local plants. Knowledge of their chemical composition and biological activities can contribute to the creation of natural-based products usefully employed in human health. In this work, we analyzed Paederia grandidieri leaves extracted with diverse eco-compatible procedures and subjected to chemical, microbiological, and cellular compatibility assays. Fresh leaves of P. grandidieri were harvested in southern Madagascar, dried and powdered. Distilled water and ethanol at different temperatures and times were used for extraction. Polyphenol composition, antioxidant, antibacterial, prebiotic and cytotoxic properties of the extracts were analyzed. The aqueous extracts contained higher levels of flavan-3-ols and flavanones, while the hydro-alcoholic extracts were richer in flavonols and flavones. The aqueous extracts showed the highest total phenolic and total flavonoid contents, and antioxidant activity, but lower cytotoxicity with respect to the hydro-alcoholic extracts. None of the extracts had significant antibacterial effects against selected oral pathogens, but prebiotic effects against Streptococcus salivarius were detected. This study indicates that P. grandidieri possesses significant biological properties which make it a suitable candidate for the production of pro-health products for the oral cavity.

The susceptibility of seven species of chestnuts to the orchid leafminer was studied in the arboretum of the Main Botanical Garden of the Russian Academy of Sciences (MBG RAS), taking into account their interspecific characteristics. Using pheromone delta traps, the highest number of moths was shown for A. hippocstanuam and the lowest for A. chinensis. A number of anatomical parameters of leaves were investigated, such as the thickness of the epidermal cell wall and the thickness of the palisade and spongy parenchyma layers. As a result, it was shown that the most infected chestnut species had a greater thickness of the nutritious parenchyma tissue. No de-pendence was found between the degree of susceptibility to the Ohrid leaf miner and such indi-cators as the content of chlorophyll a + b and carotenoids in the leaves of seven species of chestnuts. Nevertheless, resistance of different species of the genus Aesculus to C. ohridella under increased tannin content in leaves has been shown. Evaluation of phenolic compounds and flavonoids has not established their reliable role as repellents. The high levels of carbohydrates found during the study contributed to increased susceptibility to the Ohrid leaf miner.

Panax notoginseng (Burk.) F. H. Chen, a valuable Chinese herb in Yunnan Province, is susceptible to leaf spot caused by Boeremia linicola. Nonetheless, effective control methods against leaf spot in P. notoginseng are lacking. We surveyed understorey P. notoginseng bases in Lincang, Lancang, and Xundian, with incidence rate reaching 80% and an average disease index of 25 in severe cases. We found that pathogenic bacteria overwinter in infected leaves and soil, with conidia and conidiophores serving as the primary infection source. The strain B. linicola LYB-2 showed the best growth at 20°C and pH 5.0. We also screened control agents using the plate and face-off culture methods. Tetramycin exhibited 38.56–74.67% inhibition with an EC50 of 0.60 mg/L. Copper abietate showed 3.10–34.76% inhibition with an EC50 of 3,120.05 mg/L. Trichoderma atroviride and T. harzianum exhibited inhibition rates of 52.48% and 44.19%, respectively, while Bacillus subtilis reached 78.45%. Field tests demonstrated significant control effects with 20% copper abresinate, B. subtilis, T. virenceae BH-10 preparation, and 0.3% tetramycin, achieving 82.57%, 72.56%, 71.3%, and 70.2% inhibition, respectively. These findings support their eco-friendly use for preventing and controlling leaf spot disease in P. notoginseng forests; we provide vital scientific evidence for disease prediction and green control strategies.

: This study focuses on optimizing chlorophyll extraction techniques and strategically placing leaf disks to enhance chlorophyll concentration in sesame (Sesamum indicum L.) leaves. Thirty sesame genotypes, categorized into light green (LG), middle green (MG), and deep green (DG) pigment groups based on leaf coloration, were selected from a larger pool of field-grown accessions. The investigation involved determining optimal SPAD value index measurements, quantifying pigment concentrations, exploring extraction solvents, and selecting suitable leaf disk positions. Significant variations in chlorophyll content were observed across genotypes, greenness categories, and leaf disk positions. The categorization of genotypes into DG, MG, and LG groups revealed a correlation between leaf appearance and chlorophyll content. The study highlighted a consistent relationship between carotenoids and chlorophyll, indicating their role in adaptation to warm environments. Examination of leaf disk positions revealed a significant chlorophyll gradient along the leaf blade, emphasizing the need for standardized protocols. Chlorophyll extraction experiments identified DMSO and 96% ethanol, particularly the later incubated for 10 minutes at 85°C, as effective choices. This recommendation considers factors like cost-effectiveness, time efficiency, safety, and environmental regulations, ensuring consistent and simplified extraction processes. For higher chlorophyll extraction, focusing on leaf tips and the 75% position along the sesame leaf blade is suggested, consistently yielding higher chlorophyll content. Our findings therefore, underscores insightful knowledge on chlorophyll gradients and encouraging for standardized protocols, that enabling researchers to refine experimental designs for precise and comparable chlorophyll measurements. Recommended solvent choices ensure reliable outcomes in plant physiology, ecology, and environmental studies.

Leaf springs are critical components for the railway vehicle safety in which they are installed. Although these components are produced in high-strength alloyed steel and designed to operate under cyclic loading conditions in the high-cyclic fatigue region, their failure is still possible to occur and hence lead to economic and human catastrophes. The aim of this document is precisely to characterise the mechanical crack growth behaviour of the 51CrV4 spring steel representative of leaf springs under cyclic conditions, that is, crack propagation in mode I. The common fatigue crack growth prediction models (Paris and Walker) considering the effect of stress ratio and parameters such as propagation threshold, critical stress intensity factor and crack closure ratio are also determined using statistical methods, which resulted in good approximations with respect to the experimental results. Lastly, the fracture surfaces under the different test conditions were analysed using SEM, with no significant differences to declare. As a result of this research work, it is expected that the developed properties and fatigue crack growth prediction models can assist design and maintenance engineers in understanding fatigue behaviour in the initiation and propagation phase of cracks in leaf springs for railway freight wagons.

The protection of grapevine from the diseases is now particularly focused on the development of environmentally friendly strategies. Although sulphur is not in itself a toxic substance, a large use towards powdery mildew, in organic vineyards especially, may cause problems to environment, health and safety of viticultural workers and plant phytotoxicity. The activity on powdery mildew of sulphur applied at doses reduced up to 50% in tank mixtures with silicates and Equisetum arvense based products, never tested on grapevine, was assessed. Two-year trials were carried out applying the products every 7-9 days in the period of greatest risk for spring ascosporic infections, in two organic vineyards in the Abruzzo region, Italy. The effectiveness towards the disease of the mixtures was demonstrated, often to a greater extent than that of full-dose sulphur, normally used in the vineyards. Furthermore, in vines treated with the mixtures, increases in yield quantity and quality, such as higher contents of soluble solids and lower levels of total acidity, have been shown. Due to the strong efficacy demonstrated on the leaves, silicon-based products + sulphur mixtures can also be recommended in eradicative applications, aimed at reducing the inoculum of the disease. The great activity of the mixtures demonstrated in this study suggests confirmation through further investigations on different cultivars and vine-growing areas at different levels of infection, also testing the effectiveness of individual silicon products against the disease.

The chloroplast is important organelle responsible for photosynthesis and various bio- synthetic functions essential to green plants. However, the regulatory mechanism of chloroplast development remains elusive. Here, we characterized a mutant with defective chloroplasts in rice (Oryza sativa L.) named it albino and lethal 13(osal13), which exhibited a distinct albino phenotype in leaves, eventually leading to seedling lethality. Molecular cloning revealed that OsAL13 encodes a novel protein in rice for which no homologous gene or known conserved domain was found. Analysis of the biological characteristics showed that the RNAi2 leaf was completely albino phenotype, the root and shoot lengths were significantly inhibited compared with wild type. Transmission electron microscopy indicated that RNAi2 were defective in chloroplast development. Overexpression of OsAL13 resulted the photosynthetic rate increase, So the total grain number per panicle also would be significantly increase. Besides, OsAL13 was located in chloroplast and constitutively expressed in various tissues, including green and non-green tissues. In addition, disruption of OsAL13 resulted in altered expression of plastid-encoded polymerases and nuclear-encoded chloroplast genes, OsAL13 also affected hormone levels in plant tissues. Therefore, our results provide new insights into the role of OsAL13 in regulating chloroplast development in rice.

Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.

Apple chlorotic leaf spot virus (ACLSV) is a widespread, deleterious and the most damaging pathogen of fruit tree plants including domesticated apple (Malus domestica) ―a great threat to apple production worldwide. The positive-sense single-stranded RNA genome of ACLSV (7.5 Kbp) encodes three proteins: RNA polymerase (Rep), movement protein (MP) and coat protein (CP). RNA interference (RNAi)-mediated antiviral innate immunity is a key sequence-specific biological phenomenon in eukaryotes to control plant viruses. The aim of this study was to analyze apple (M.domestica) locus-derived microRNAs (mdm-miRNAs) with predicted potential for targeting the ACLSV +ssRNA-encoded mRNAs, using ‘four algorithms’ approach. The ultimate goal in this research is to mobilize the in silico endogenous predicted mdm-miRNAs to trigger RNAi catalytic pathway experimentally and generate apple tree varieties for evaluating potential antiviral resistance monitoring capability and capacity for ACLSV. Experimentally validated mature apple (M.domestica, 2n = 2X = 34) mdm-miRNAs (n = 322) were acquired from miRBase database and tested for alignment with the ACLSV genome. Of the 322 targeting mature locus-derived mdm-miRNAs investigated, nine apple mdm-miRNA homologs (mdm-miR395k, mdm-miR5225c and mdm-miR7121 (a, b, c, d, e, f, g, h) were predicted by all ‘four algorithms’. Only fifty eight mdm-miRNAs were predicted consensus binding sites by union of consensus between two algorithms. The miRanda, RNA22, TAPIR algorithms predicted binding of mdm-miR395k at unique nucleotide position 4691, as the most effectively interacting mdm-miRNAs in targeting the ORF1 sequence. In order to validate target prediction accuracy, whether the apple mdm-miRNAs might bind predicted ACLSV mRNA target(s), we created an integrated Circos plot. Genome-wide in-silico-predicted miRNA-mediated target gene regulatory network validated interactions that warrant in vivo analysis. The current work provides valuable evidence and biological material for generating ACLSV-resistant apple varieties.

The few studies dealing with leaf phenological responses to elevated nutrients in forest trees have given ambiguous results, i.e., while some reported delayed leaf-out and autumn leaf senescence, others reported advanced leaf phenology caused by increased nutrition. This study aimed to determine the effects of experimentally increased phosphorus (+P) on the leaf phenologies of two juvenile provenances of common beech and sessile oak. Other objectives were to determine whether there are interspecies differences as well as intraspecies variations. Saplings were excavated in two mixed beech-oak stands and transplanted into four wooden boxes filled with a commercial soil substrate. Phosphorus fertilizer was added to two of the boxes, while the remaining boxes served as controls. Both species responded to +P treatment with advanced autumn leaf senescence in the first year of the experiment. Leaf senescence in common beech began significantly earlier, while in both species, the process was accelerated compared to that in the control. In the second year, the leaf senescence response to +P treatment was even more pronounced in both species. The +P effect on leafing phenology was absent in both common beech provenances and in an oak provenance. However, the other oak provenance showed advanced leafing, indicating the existence of intraspecies differences.

Zinc oxide (ZnO) nanoparticles (NPs) were synthesized using Rosmarinus officinalis leaf extract at 80 ° C (ZnO-80) and 180 ° C (ZnO-180). The biosynthesized ZnO NPs were characterized and their photocatalytic activity was evaluated for the degradation of methylene blue (MB) and crystal violet (CV) under sunlight irradiation. The results of the characterizations by XRD, TEM and SEM showed that the size of the NPs of ZnO-80 was smaller than that of ZnO-180 which exhibited flakier agglomerated spherical structures. Photocatalytic tests showed ZnO-80 which was prepared by a cheap and easy procedure compared to ZnO-180 effectively degrades MB and CV dyes under sunlight. The superior performance of ZnO-80 over ZnO-180 can be explained by the differences in their textural properties. This is because ZnO-80 has a smaller crystallite size, a specific surface area and a higher pore volume than ZnO-180. Fourier-transform infrared spectroscopy (FTIR) analyzes revealed that both samples contained an adsorbed carboxylate group (COO-), and accordingly a mechanism was proposed for the formation of ZnO NPs that include the carboxyl group.

Cocoa is an important commodity crop not only to produce one of the most complex products such as chocolate from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried and grinded to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm and the aroma profile considering six main aromas as targets. The ANN model rendered high accuracy (R = 0.82; MSE = 0.09) with no overfitting. The model was then applied to a satellite image from the whole cocoa field studied to produce canopy vigor and aroma profile maps up to the tree-by-tree scale. The tool developed could aid significantly the canopy management practices in cocoa trees that have a direct effect on cocoa quality.

This study evaluated the impact of the level of supplementation of Moringa oleifera leaves on the nutritional (proximate, mineral and vitamin content) as well as on the antioxidant capacities of smoothies made from a blend of pineapple, banana and apple. The beverage (smoothies) were supplemented with Moringa oleifera leaves at 1.5, 3.0 and 4.5% levels and evaluated for proximate, antioxidant (ferric reducing antioxidant power, FRAP and 2,2-Diphenyl-1-picryhydrazy, DPPH), mineral, vitamin, physicochemical and quality acceptability. The results showed that the moisture content of the beverage ranged between 49.24 – 78.62%, total ash: 1.01 – 9.71, crude fiber: 5.14 – 9.39%, crude fat: 0.72 – 1.86%, crude protein: 5.47 – 19.37% and carbohydrate: 3.65 – 16.99%. Calcium (12.03 -15.53) and potassium (17.22 -25.38) were the predominant mineral elements when compared to magnesium (1.51 – 3.05) mg/L. The vitamin contents ranged between 2.5 – 10.8 and 0.15 – 0.93 mg/L for vitamin C and E, respectively. Total phenolic contents ranged between 4.68 – 6.18 mg/ml while the total flavonoid contents ranged between 0.01 – 0.14 mg/ml. The radical scavenging abilities (DPPH) of the samples ranged between 16.05 – 88.77% while the ferric reducing antioxidant power (FRAP) ranged between 0.38 – 7.36 mg/ml. The brix values showed high sugar contents (15 – 18%) while the pH results showed that the sample was almost neutral at a range between 6.3 and 6.5. The overall quality (sensory) acceptability of the different quality parameters evaluated indicated that the control sample was more preferred. While supplementation with Moringa oleifera leaves significantly affected the sensory parameters, its addition nevertheless offers a potential avenue to obtain additional nutrients besides its improved antioxidative properties.

Wheat is one of the most important crops to ensure food production globally. Understanding the mechanism of leaf senescence in wheat plays a crucial role in improving its productivity and resilience under varying stress scenario. In this study, we investigated biochemical, functional, and ultrastructural changes during leaf senescence in wheat genotypes with contrasting drought tolerance. For this, key parameters such as chlorophyll and total protein content, membrane stability, malondialdehyde level, and the activity of antioxidant enzymes (superoxide dismutase, ascorbate peroxidase, guaiacol peroxidase, benzidine peroxidase, catalase) were comparatively analyzed during both natural and drought-induced senescence. Additionally, the expression of superoxide dismutase isoform genes functioning in different cellular compartments was studied, alongside ultrastructural changes in flag leaves. The experiments involved genotypes of bread wheat (Triticum aestivum L.) and durum (Triticum durum Desf.) wheat. After the booting stage, irrigation was discontinued for drought-treated plants. Flag leaves were sampled at 7, 14, 21, 28, and 35 days after anthesis. The drought-tolerant genotypes exhibited slower chlorophyll degradation, lower lipid peroxidation levels, higher membrane stability, and more robust antioxidant responses, maintaining cellular function longer than the sensitive genotypes, which showed accelerated leaf senescence. Transcript levels of FeSOD increased significantly post-flowering but declined as senescence progressed, while MnSOD expression rose towards the end of ontogenesis. Ultrastructural analysis revealed progressive damage to chloroplast membranes, thylakoid structures, and mesophyll cell walls under stress conditions. These findings contribute to a deeper understanding of the physiological and molecular responses of wheat to drought stress, offering potential targets for improving crop performance in water-limited environments.

Diatraea saccharalis (Fabricius) is one of the main pests of rice crops and its early detection, that is, before the plants show damage, is essential to avoid yield losses and define effective and rational control. This work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels. Between 2019 and 2020, two experiments were conducted in a protected environment with the cultivars IR 40 and BR IRGA 409, which presented, in a previous study, different resistance reactions. Rice plants grown in pots were manually infested with first-instar larvae of D. saccharalis, from 0 to 10 caterpillars/plant, with the plants kept in cages covered with voile fabric throughout the test. With the adjustment of regression models, it was noticed that the leaf surface temperature is related to the level of infestation and could be used to detect which IR 40 is susceptible.

This study explored the effectiveness of natural additives in prolonging the life of cut roses. A Completely Randomized Design (CRD) was used, with four different treatments: distilled water (control), a sucrose solution, moringa leaf extracts, and a combination of sucrose and moringa. The roses were observed over eight days to assess various factors such as leaf drop, stem rotting, bent necks, petal drop, flower condition, and petal color. The results showed that the combined treatment of sucrose and moringa led to the highest number of leaf drops, with 15 leaves dropping by day five. Distilled water resulted in 12 dropped leaves, moringa extract caused four leaves to drop, and sucrose had the least impact with only two leaves dropping. The combination treatment also caused the most significant stem rotting (32.75 mm) and the most bent necks (14 roses). In contrast, roses treated with sucrose or moringa separately had better outcomes, with the least bent necks and minimal leaf drops. For petal drop, the combined treatment again had the worst results, with eight petals dropping, followed by distilled water with six petals. The overall flower condition was poorest with the combined treatment, scoring 1 (completely open/damaged), while roses treated with sucrose or moringa separately had better scores, indicating they remained fresher. Petal color was also most affected by the combined treatment, which resulted in a light brown color, whereas the separate treatments maintained a darker red hue. Hence, while sucrose and moringa are beneficial when used individually, their combination negatively impacts the longevity and appearance of cut roses.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

To date the role of green leaf volatiles has been mainly constrained to their role in protecting plants against pests and pathogens. However, increasing evidence suggests that among the stresses that can significantly harms plants, GLV can also provide significant protection against an array of those including heat, cold, drought, light, and salinity. But while the molecular basis for this protection is still largely unknown, it seems obvious that a common theme in the way GLV work is that most if not all of these stresses are associated with physical damage to the plants, which in turn is the major event responsible for the production of GLV. Here, I summarize the current state of knowledge on GLV and abiotic stresses and provide a model that explains the multifunctionality of these compounds.

Cercospora beticola Sacc. is an ascomycete pathogen that causes Cercospora leaf spot in sugar beets (Beta vulgaris L.) and other related crops. The disease symptoms are characterized by circular lesions with an ash-gray centre and dark brown to purple margins on the leaves. It can lead to significant yield losses if not effectively managed. This study aims to characterize and assess the impact of bacterial biological control agents against C. beticola. Following a dual-culture screening, 18 bacteria exhibiting over 50% inhibition were selected, with six of them demonstrating more than 80% control. Identification of the bacteria was carried out through sequencing of the16S rDNA, revealing potentially 12 species belonging to six genera including Bacillus which was represented by four species. Additionally, the bacteria underwent in-depth characterization of biochemical and molecular properties, as well as plant-growth promotion. PCR detection of genes responsible for producing antifungal metabolites revealed that 83%, 78%, 89%, and 56% of the selected bacteria possessed the bacillomycin, iturin, fengycin, and surfactin encoding genes, respectively. Infrared spectroscopy analysis confirmed the presence of lipopeptide structure in the bacteria supernatant filtrate. Subsequently, the bacteria were assessed for their effect on sugar beet plants in controlled conditions. The bacteria exhibited notable capabilities, promoting growth in both roots and shoots, resulting in significant increases in root length, weight, and shoot length. The field experiment with four bacterial candidates demonstrated good performance against C. beticola compared to difenoconazole fungicide. These bacteria played a significant role in disease control, achieving a maximum efficacy of 77.42%, slightly below the 88.51% efficacy attained by difenoconazole. Additional field trials are necessary to verify the protective and growth-promoting effects of these candidates, whether applied individually, combined in consortia, or integrated with chemical inputs in sugar beet crop production.

Drought stress is a major production constraint in crops globally. Crop wild relatives are important source of resistance and tolerance for both biotic and abiotic stresses, respectively. A breeding program was initiated to introgress drought tolerance in sunflower through hybridization between the wild species Helianthus argophyllus and the cultigen pool of H. annuus. Selection was carried out in the F2 to F5 segregating populations for high cuticular waxes, smaller leaf area, single heading and high oil content. The selected F5 breeding lines were compared with non-adapted elite sunflower germplasm in a replicated randomized complete block research project using different water treatments comparing fully irrigated (T0), with 75 (T1), 50 (T2) and 25% (T3) irrigation treatments. The comparison between the two types of germplasm showed that drought tolerant breeding lines had comparatively lesser decreases in leaf area, shoot weight and root shoot ratio 10 under the various drought treatments. Leaf area of the drought selected F5 lines were smaller than that of the elite germplasm under controlled irrigated treatments, but they maintained their leaf area under the drought treatments. Several drought resistant promising lines D-2, D-5, and D-27 were identified and showed high leaf area, great root length and increase root to shoot ratio under the highest stressed treatments (T3). Some of the lines could be directly used for the development of drought tolerant hybrids. Combining ability test indicated D-27 (F7) as good general combiner for seed yield plant−1 and oil contents when mated with male line RSIN.82. These hybrids could help to minimize seed yield losses due to water stress and to achieve profitable cultivation of sunflower in 18 arid regions of Pakistan.

The production of coffee leaf tea (Coffea arabica) in El Salvador and the influences of processing steps on non-volatile compounds and volatile aroma-active compounds were investigated. The tea was produced according to process steps of conventional tea (Camellia sinensis) with available possibilities on the farm. Influencing factors were the leaf type (old, young, yellow, shoots), processing (blending, cutting, rolling, freezing, steaming), drying (sun drying, oven drying, roasting) and fermentation (wild, yeast, Lactobacillus). Subsequently, the samples were analysed for the maximum levels of caffeine, chlorogenic acid, and epigallocatechin gallate permitted by the European Commission. The caffeine content varied between 0.37 g/100 g dry mass (DM) and 1.33 g/100 g DM, the chlorogenic acid between not detectable and 9.35 g /100 g DM and epigallocatechin gallate could not be detected at all. Furthermore, water content, essential oil, ash content, total polyphenols, total catechins, organic acids, and trigonelline were determined. Gas chromatography—mass spectrometry-olfactometry and calculating of the odour activity values (OAVs) were carried out to determine the main aroma-active compounds, which are β-ionone (honey-like, OAV 132-927), decanal (citrus-like, floral, OAV 14-301), α-ionone (floral, OAV 30-100), (E,Z)-2,6-nonadienal (cucumber-like, OAV 18-256), 2,4-nonadienal (melon-like, OAV 2-18), octanal (fruity, OAV 7-23), (E)-2 nonenal (citrus-like, OAV 1-11), hexanal (grassy, OAV 1-10), and 4-heptenal (green, OAV 1-9).

Multi- and hyper-spectral, multi-angular top-of-canopy reflectance data call for an efficient retrieval system which can improve the retrieval of standard canopy parameters (as albedo, LAI, fAPAR), and exploit the information to retrieve additional parameters (e.g. leaf pigments). Furthermore consistency between the retrieved parameters and quantification of uncertainties are required for many applications. % (2) methods We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL, PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (BRDF, moisture), and a cloud contamination model. The inversion is gradient based and uses codes % created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. For most of the sites, the PhenoCam images support the OptiSAIL retrievals. The system is computationally efficient with a rate of 150 pixel per second (7 millisecond per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals, puts real-time processing with this kind of system into reach, seamlessly extends to hyper-spectral and multi-sensor retrievals, and promises to be a good platform for sensitivity studies. The incorporated cloud and snow detection adds to the robustness of the system.

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

The basis of orchard production are rootstocks that reduce the vigor of sweet cherry trees. However, not all rootstocks for this species can be easily propagated using traditional methods by stooling or cutting. Some of these must be propagated using the in vitro culture method. This results in their high price and, consequently, the price of maiden sweet cherry trees. The experiment assessed the growth of maiden trees of selected sweet cherry varieties ('Bellise', 'Earlise', 'Lapins', 'Vanda') depending on the method of the semi-dwarf GiSelA 5 rootstock propagation. Additionally the intensity of life processes taking place in the obtained maiden trees was also examined. One type of this rootstock, obtained by cheaper method from softwood cuttings, was compared with the another propagated using the in vitro method. There were no significant differences in the percentage of maiden trees obtained in both types of rootstocks propagation method and in the two years of study. The efficiency of maiden trees varied little and ranged from 77.43% to 87.74% depending on the factors considered. The vigor of maiden trees growth in the first year of the study was stronger than in the second one. Only the diameter of maiden trees stem varied (from 7% to 39%), depending on the variety considered. With the exception of one variety, maiden trees produced on rootstock propagated from stem cuttings were characterized by a much larger diameter for the three varieties, ranging from 23% to 29% compare to in vitro. Also with this method of propagation, the rootstocks had a better developed root system and the maiden trees had a greater fresh mass. However, most often, no significant differences were found between the methods of propagation, only when comparing the years of research. The activity of life processes of maiden sweet cherry trees varied and no constant regularities were found. The only regularity observed was that maiden trees in the second year of the experiment were more often characterized by lower levels of the tested life parameters (Pn, E, C and Int.CO2), which was associated with worse growth results.

The feasibility of recovering bioactive antioxidants from Phyllantus emblica leaves using ethanol -water mixture (0 – 100%) and heat-assisted extraction technology (HAE-T) was investigated in this present study. To this effect, the process parameter of operating temperature (OT: 30 -50 oC), solid -to-liquid ratio (S:L: 1:20 - 1:60 g/mL) and extraction time (ET: 45 - 180 min) were investigated on the extract total phenolic content (TPC, mg GAE/g d.w), yield (EY, %) and antioxidant activity (μM AAE/g d.w) by employing the Box-Behnken experimental design (BBD) available in response surface methodology (RSM). Multi-objective process optimization using the desirability function algorithm to determine set of process variables that simultaneously optimize process responses of TPC, EY and AA was also investigated. The recovery process was thereafter modeled with BBD-RSM and multi gene genetic programming (MGGP) algorithm and model reliability assessment via Monte Carlo simulation were conducted for the best performing set of models for predicting TPC, EY and AA values. The HPLC characterization of the recovered extract was carried out to ascertain the phenolic compound constituents of the Phyllantus emblica leaf extract. The 50% ethanol solution was found to be the best for the optimal extraction of antioxidants from Phyllantus emblica leaves. The HAE-T was suitable for recovering bioactive extract from Phyllantus emblica leaves and investigated process parameters have impacts on the recovery process. The process parameters that simultaneously gave the optimum EY of 21.6565%, TPC of 67.116 mg GAE/g and AA of 3.68583 µM AAE/g were OT of 41.61 oC, S:L of 1:60 g/mL and ET of 180 min. The HPLC extract profiling revealed that the bioactive constituents present in the recovered extract were betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid. Both the developed BBD-RSM and evolved MGGP-based models predicted the observed process response of TPC, EY and AA satisfactorily with BBD-RSM models marginally better in their predictions. The reliability results showed that the BBD-RSM models predicted TPC, EY and AA values with high certainty of 99.985%, 97.569% and 98.661%, respectively.