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

Abstract

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

Comparative Analysis of Plant Growth-Promoting Rhizobacteria (PGPR) and Chemical Fertilizers on Quantitative and Qualitative Characteristics of Rainfed Wheat

“…Soil (tillage, crop rotation, mulching, and manure fertilizers) and crop (selection of better crops, intercropping/crop rotation, timing of operations, weeds, and pest management) management are important practices to maximize the depth and density of roots and increase plant water uptake capacity. Among them, soil fertility management, through organic and inorganic fertilizers, is key to crop water uptake capacity and is a prerequisite to crop growth [26,27]. However, in rainfed cultivation, the response of a crop to fertilizer applications is heavily reliant on water availability [28].…”

Precision Nitrogen Fertilization for Opium Poppy Using Combined Proximal and Remote Sensor Data Fusion

“…This suggests that relying solely on NDVI for decision making on N fertilization should be given special care, particularly regarding the selection of the best crop growth stage for NDVI data collection [61,62]. The use of time-series NDVI data instead of a single NDVI measurement is highly recommended [18], although this was not the case in this study.…”

Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation

“…Achieving sustainable intensive agriculture requires a better adjustment of the inputs used (seeds, nutrients, water, etc.). Specific farm tools based on historical satellite imagery, using vegetation indices, together with other remote sensing tools closer to the crop, make it possible today to establish different management zones on a plot [192]. Management zones are a breakthrough in the field of precision farming, however, even more so will be automated management zones using new artificial intelligence techniques.…”