Chatzidavid, D.; Kokinou, E.; Gerarchakis, N.; Kontogiorgakis, I.; Bucaioni, A.; Bogdanovic, M. Delineation Protocol of Agricultural Management Zones (Olive Trees, Alfalfa) at Field Scale (Crete, Greece). Preprints2024, 2024081118. https://doi.org/10.20944/preprints202408.1118.v1
APA Style
Chatzidavid, D., Kokinou, E., Gerarchakis, N., Kontogiorgakis, I., Bucaioni, A., & Bogdanovic, M. (2024). Delineation Protocol of Agricultural Management Zones (Olive Trees, Alfalfa) at Field Scale (Crete, Greece). Preprints. https://doi.org/10.20944/preprints202408.1118.v1
Chicago/Turabian Style
Chatzidavid, D., Alessio Bucaioni and Milos Bogdanovic. 2024 "Delineation Protocol of Agricultural Management Zones (Olive Trees, Alfalfa) at Field Scale (Crete, Greece)" Preprints. https://doi.org/10.20944/preprints202408.1118.v1
Abstract
This study proposes a flexible and adaptable protocol for the establishment of agricultural management zones that utilises remote sensing, ground truthing (apparent electrical conductivity and soil sampling), the IRRIGOPTIMAL® system and machine learning. This protocol could contribute significantly to the rational use of inputs (water, fertilizers and pesticides) and to the further efficient development of an optimal irrigation system with variable irrigation rates. The methodology to develop this protocol was applied to olive and alfalfa plots in Heraklion (Crete, Greece) to monitor soil and crop responses for the period 2022-2024. Spatial and temporal assessment of selected soil and plant parameters (moisture, photosynthetic activity) using ground and vegetation reflectance mapping by satellites and unmanned aerial systems provides important information in both the pre- and main phases of the management zone delineation. Geophysical methods such as electromagnetic induction, applied in the main phase of management zone delineation, provide a robust technique to determine the spatial and temporal distribution of apparent electrical conductivity. The Random Forest machine learning model was found to be most suitable for predicting soil electrical conductivity based on satellite-derived salinity indices and ground electromagnetic induction. Finally, the IRRIGOPTIMAL® system provides real-time monitoring of a variety of weather and soil parameters to determine the optimal cultivation of crops based on the creation of agricultural management zones.
Keywords
Precision Agriculture; Soil and crop response; Remote Sensing; Geophysical mapping; Machine learning; IRRIGOPTIMAL® system
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
