Plant disease diagnostics using an unmanned aerial vehicle with low-power computing modules

Research is presented on solving a pressing problem in agricultural engineering: the development of an energy-efficient onboard system for the automatic detection of phytopathological plant diseases using computer vision and deep learning methods. The study was conducted in the context of the growing need for intelligent agricultural monitoring technologies capable of functioning in field conditions with limited computing resources. The object of the study is wheat crops examined in the agricultural landscapes of the Republic of Bashkortostan in various phases of vegetation. The scientific novelty lies in the construction of a modified neural network detector architecture based on a lightweight version of YOLO, including low-cost convolutional blocks GhostConv and MBConv, attention modules SE and CBAM, as well as a multi-level feature aggregation structure BiFPN with an additional output P2 to increase sensitivity to small-scale disease symptoms. Unlike the basic YOLOv5s architecture, the proposed solution is optimized for operation on NavQ Plus, Jetson TX2, and Raspberry Pi 4 computing modules. The model was trained on a sample of 7,500 images manually labeled by agricultural specialists for brown and yellow rust. To validate the performance, key metrics were used: Precision, Recall, F1-score, average IoU, FPS, and power efficiency (FPS/W). The experimental results showed the following achievements: F1-score up to 0.978, IoU up to 0.82, processing speed up to 16.8 FPS and power efficiency of 2.7 FPS/W on the NavQ Plus platform. A comparative analysis with the YOLOv5s baseline model confirmed the superiority of the proposed architecture across all key parameters. The developed model can serve as the foundation for building intelligent precision farming solutions, enabling early disease detection and adaptive application of crop protection products with minimal energy and computational costs.

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