Unmanned Aerial Vehicle (UAV) Applications for Crop Health Mapping and Estimation.
Achieving zero hunger, the second goal of the sustainable development goals (SDGs), requires addressing challenges in food production, particularly crop pests and diseases that cause significant yield losses. Timely and precise detection, monitoring, and mapping of crop health issues are crucial for achieving zero hunger, especially in regions like Africa facing hunger and scarcity. To enhance economic growth and Agricultural sustainability, precision agriculture plays a vital role. This research aims to develop a low-cost geospatial automated system for assessing crop health using visible light unmanned aerial vehicles (UAVs). The study area, measuring approximately 21 hectares, is located between the Federal University of Technology’s main campus and Garatu village.
Visible light camera images with 1.0cm resolution were acquired, and image processing involved three stages: Initial processing, generation of point cloud mesh, and digital terrain model (DSM), orthophoto, and index map generation using algorithms like Scale Invariant Feature Transformation (SIFT), Bundle Block Adjustment (BBA), and Structure from Motion (SFM), respectively. Various vegetation index maps (NDVI, VDVI, NGRDI, NExG maps) were derived from the orthophoto to estimate crop healthiness. The sample crops exhibited a relatively fair healthy characteristic across the vegetation indices, with NDVI and VDVI proving to be the most optimal and reliable. Statistical analysis was conducted to correlate the NDVI values with the respective tissue test results, demonstrating that photogrammetry can offer highly reliable estimates of crop health, particularly for cassava and rice, among others, with a 100% chance of obtaining consistent results. This research showcases the potential of UAV-based precision agriculture in contributing to food security and sustainable development, particularly in regions with heightened challenges related to hunger and scarcity.
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