Towards improving the precision agriculture management of the wheat crop using remote sensing: A case study in Central Non-Black Earth region of Russia

Smart management of the wheat crop requires understanding the various variables affecting crop quality and quantity. The use of remote sensing data contributes to improve the application of precision agriculture (PA).The current paper aims to evaluate the efficiency of remote sensing in monitoring wheat growth and enhance the management practices. Three cultivation technologies for winter wheat were implemented: basic (CT1), intensive (CT2), and highly intensive (CT3) were investigated. Sentinel-2 with a resolution of 10 m was used to monitor the change in wheat growth under different management systems during 2019. The following variables: yield quantity, grain quality (measured protein and gluten), in addition, five vegetation indices: Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI),Green Chlorophyll Index (GCI), Green Leaf Index (GLI) were retrieved during the growing season. The results showed that, GCI has the highest performance in predicting crop yield where r was 0.98. In addition, the SAVI and NDVI have the same performance; r was 0.96 for both protein and gluten contents. The yield production of CT3 has increased by 3 t/ha, in addition, the grain quality was superior compared to the CT1. The economic efficiency results showed that the CT3 was the most profitable for Moscovskaya 40 variety (WV1) with 2,72 Payback. For Nemchinovskaya 17 variety (WV2), the most profitable cultivation technology was the CT1 and CT2 with 2,44 Payback, and for the new variety Nemchinovskaya 85, the CT2 was the most profitable with the Payback of 3,03. Finally, remote sensing shows the spatial variation in crop growth, which enhances crop management to achieve optimal production in terms of quantity and quality.