Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

Enhancing digital and precision agriculture is currently inevitable to overcome the economic environmental challenges of in 21st century. The purpose this study was generate compare management zones (MZ) based on Sentinel-2 satellite data for variable rate application mineral nitrogen wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield crop availability. Three were applied, including (1) a modified “RS- threshold-based clustering”, (2) “hybrid-based, unsupervised which from sources combined MZ delineation, (3) “RS-based, clustering”. Various processing methods machine learning used model development. Statistical tests such as Paired Sample T-test, Kruskal–Wallis H-test Wilcoxon signed-rank test applied evaluate final delineated maps. Additionally, procedure improving information about phenological phases occurrence agricultural drought implemented. results showed that agronomy climate enables optimizing delineation. integration prior knowledge new conditions (drought) image selection tested effective use models. Lack led infeasibility obtaining optimal results. Models solely rely are comparatively less expensive than hybrid sensing-based enable delineating fertilizer recommendations temporally closer fertilization times.