Life Cycle Cost Analysis is a method used to assess long-term economic efficiency among equivalent competing processes or products. The purpose of the paper is to investigate the nature and level of costs for an organic orchard located in Southern Romania, using a complex approach covering the entire chain of production, through the life span. The research results, based on a dynamic analysis and an integrated evaluation of the orchard's performance, have been ranked on investment and operational costs, broken down on three categories (establishment and production, post-harvest and transport costs). The highest cost, representing 151726 euros/ha/20 years, about 52.72% of the total operational costs and 50.4% of the total farm costs/ha/20 years, was recorded in exploitation stage. The scenarios for the sensitivity analysis considered different levels of average yield (40, and, respectively 60 tons/ha) with different rate of sold production (85%, optimistic scenario; 70%, pessimistic scenario). The hotpoints identified at the production stage were use of agricultural machinery, several pesticides, the costs of seedlings, anti-hail net, plastic boxes and labor cost, while at the post-harvest stage, were those related to labor and energy consumption. The transport stage has had important costs with tractor operation and the track.

In this paper, we propose a system to create high-precision maps using UAV-LiDAR and to determine the location of individual fruit trees (apple trees) on the maps. The system is based on a UAV-LiDAR system that flies over an actual orchard. A UAV was flown over an actual orchard, and the point cloud of the onboard LiDAR and the location information of RTK-GNSS were obtained. The system records the LiDAR point cloud and RTK-GNSS position information. Automated software processes point cloud data offline and Automated software processes point cloud data offline and automatically segments each apple tree in the map. The RTK-GNSS position information is used for the segmented trees. The positional information obtained from RTK-GNSS was georeferenced to the segmented trees without using ground evaluation points. As a sample, location information was obtained from trees using the Quasi-Zenith Satellite System (QZSS) MICHIBIKI. The positional accuracy of the trees was evaluated using the positional information obtained from the Quasi-Zenith Satellite System MICHIBIKI as a reference. As a result, the alignment accuracy was sufficient to identify individual fruit trees.

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for monitoring the health and growth of crops in orchards. Another important objective was to use low-cost sensors that accurately measure ultraviolet solar radiation. The device, which needs to be attached to the commercial DJI Mini 4 Pro drone, should be small in size, portable, and have very low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions are drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the results of the plant condition analysis with proposed models in the Geographic Information System (GIS) environment were also presented. The visualization of maps indicating variations in vegetation condition led to the identification of problem areas like hydric stress.

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.

Ash dieback (ADB), caused by the invasive fungal pathogen Hymenoscyphus fraxineus, poses a significant ecological and economic challenge across Europe. Fraxinus excelsior (European ash), a key component of forest ecosystems, has experienced mortality rates reaching up to 85% in affected regions, severely threatening its ecological role and economic value. This study examines the relationship between the phenological traits of ash clones, particularly the timing of spring bud burst, and their susceptibility to H. fraxineus infection. The study was conducted in a clonal seed orchard located in northeastern Poland, encompassing 31 ash clones from different bioclimatic regions. Phenological observations of bud burst were carried out from early April to late May during the years 2018-2020, and crown damage and de-foliation levels were assessed multiple times throughout the growing season. The results confirm that clones with earlier bud burst exhibit significantly higher survival rates and reduced crown damage. It was found that the timing of bud burst is strongly correlated with susceptibility to ash dieback, suggesting that phenological timing is a crucial factor in the selection of genotypes resistant to ash dieback. Statistical analyses, including ANOVA and mixed models, revealed significant differences in susceptibility to infection among clones from different bioclimatic regions. Furthermore, the study high-lights the potential of breeding strategies focused on these traits to enhance the resilience of ash popula-tions. These findings are critical for the development of effective forest management practices aimed at conserving ash resources and maintaining biodiversity in the face of climate change and the ongoing spread of the pathogen.

A typical orchard's mechanical operation consists of three or four stages: lining, digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging the planting hole is the most time-consuming operation. In fruit orchards, robots' usage is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GIS), to develop an autonomous drilling machine and use the developed robot to open planting holes. There isn't a comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS software was used for designing and drawing of the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

In order to reduce the vibration of the transporter and improve the stability of the transporter, the vibration characteristics and key structure optimization research of the mountain orchard transporter are carried out. In this paper, the vibration of electric monorail transporters under different working conditions is tested, and the causes of vibration generated by monorail transporter during operation are explored. The three-dimensional model of the trailer and transmission box is established using Solidworks, and the modal vibration modes of the structure are analyzed theoretically. The modal test system is used for modal test analysis of trailers and transmission boxes. By comparing the results of finite element and experimental modal analysis, the second-order frequency of the trailer is close to the motor's rotating excitation frequency of 50 Hz at rated speed, which is easy to generate resonance. By optimizing the structure of the trailer, the second and third natural frequencies are increased to 54.79 Hz and 58.35 Hz respectively, which avoid the resonance of the trailer during operation and effectively reduce the vibration generated during the transportation of the trailer. Through vibration testing of the optimized electric monorail transporter, the results of vibration testing before and after structural optimization are compared and analyzed, and it is found that the vibration amplitudes of the transporter in X, Y and Z directions are reduced by 0.308m/s², 0.351m/s² and 0.334m/s² respectively, the running stability of the conveyer is improved.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigations. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy, and to address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. Firstly, the fruit tree trunk is identified based on the improved YOLOv4 model; secondly, the camera coordinates of the tree trunk are calculated from the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined and the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall of the improved YOLOv4 model for fruit tree trunk detection are 97.05% and 95.42%, respectively, which are 5.92 and 7.91 percentage points higher than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows, and can provide a reference for autonomous visual navigation of orchard robots.

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter row environments for agricultural machinery. The paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines is less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on four zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results show that within a range of 6m, the average relative error of distance measurement does not exceed 1%, and within a range of 9m, the maximum relative error is 2.03%. The average distance measurement time is 30 milliseconds, which can accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduces the RMSE index of absolute trajectory error compared to ORB-SLAM2 algorithm, which is less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 has a higher degree of agreement between the estimated trajectory and the true trajectory when the car is traveling in straight and circular directions. The RMSE index of the absolute trajectory error is less than 0.03m/s, and the average tracking time is 47ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper can meet the accuracy and real-time requirements of agricultural machinery positioning in orchard environments.

Towns emerged as dynamic economic and political centers during the Middle Ages, giving rise to the emergence of new social classes. As a result of these functions, a new relationship began to be forged with the rural world, which supplied towns with foodstuffs that satisfied new social demands. Archaeobotanical analysis (carpology) allows us to understand the flow of cash crops by tracing seeds and fruits produced in the countryside that were consumed in and redistributed from the towns. The study of waterlogged contexts from medieval archaeological sites in the Kingdom of Galicia (Santiago de Compostela, Padrón, and Pontevedra) has provided a set of species that played a crucial role in the economy of the urban dwellers and that possibly were related to differential access or food preferences. Evidence for fruits (grapes, chestnuts, figs, apples, and cherries, among others), garden crops (melon), and cereals (foxtail millet, rye, naked wheat, and oat) has been documented. Broomcorn millet is particularly abundant, demonstrating that it was important for subsistence. Some of the species found (medlar and turnip/grelo) are novel in the archaeobotanical literature of the medieval period in the Iberian Peninsula.

The characteristics of the Sentinel-2 mission with a decametric resolution and frequent acquisitions allow to improve the identification of crops. The majority of the studies on crop classification using RS were targeted at herbaceous and gramineous crop classes while fewer results were obtained on woody crops which present a strong variability in management practices that make their identification difficult. Thus, this study aimed to propose a rapid, accurate, and cost-effective analytical approach for the delineation of fruit orchards (OC), vineyards (VY), and olive groves (OL) in the Mediterranean (Southern France) considering two locations. A classification based on phenology metrics (PM) de-rived from temporal Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegeta-tion indices to delineate OC, VY, and a DC class gathering all remaining surfaces. The generated PM were introduced in a random forest (RF) algorithm to identify woody crops across the two sites. The method was tested on different vegetation indices, the best results being obtained with the leaf area index (LAI). To delineate OL in the DC class, the tem-poral features of the green chlorophyll vegetation index (GCVI) were found to be the most appropriated with a typical drop of the signal during the mid-season (DOY 150-250). As a final result, we obtained an overall accuracy ranging from 89-96% and Kappa of 0.86-0.95 by considering each study site and year (2016-2021), separately. This accuracy is much better than applying the RF algorithm on the LAI times series, which led to a Kappa rang-ing between 0.3 and 0.52 and demonstrates the interest of using phenological traits rather than the raw time series of the RS data. The method can be well reproduced from one year to another. Moreover, it is possible to apply the classification model of a given year to an-other, keeping good accuracy. This is an interesting feature to reduce the burden of col-lecting ground truth information. On the contrary, the use of a classification model cali-brated in one site and applied to another led to a strong degradation of the classification accuracy. Woody crop phenology is dependent on site climatic conditions as well as the cultivar and management practices that can differ from one site to another.

This research was carried out as part of a program for the conservation and improvement of the strawberry tree. Accessions’ prospecting was conducted in different Portuguese provenances. Accessions (204) were identified, and mature fruits were collected in autumn. The sugar contents in the fruit pulp (glucose, fructose, maltose, sucrose) were analyzed by HPLC and the correlation between the average sugar content, climatic classification (Thornthwaite Method and Xerothermic Index), and edaphic characteristics was investigated. The predominant sugar was fructose, which ranged from 7.89±0.55% to 17.01±1.46% (f.w.), respectively, under an Attenuated Thermomediterranean climate at limestone-derived soil and an Accentuated Mesomediterranean climate at schist-derived soil. The lowest sugar contents, linked to a reduction in photosynthesis, were found: 1) in the north, despite soil water availability, due to the low temperature, high precipitation and short photoperiod, particularly during the fruit maturation (autumn); 2) in the south, due to the excessive number of dry days, linked to low soil water availability during the active growth period, particularly at limestone zones prone to water retention. The relationship found between the total sugar content and climate classification by the Xerothermic Index allows to enhance fruit production, advise on the establishment of new orchards and restore natural areas.