Development of a Low-Power Onboard System for Spot Detection of Wheat Phytopathologies Using a Modified YOLO Architecture

Introduction. Early diagnosis of diseases of cereal crops is a key task for precision agriculture, because late disease detection results in significant yield losses and inefficient use of plant protection products. Existing phytosanitary control systems mainly focus on offline data post-processing and require substantial computational resources that limits their use on board unmanned aerial vehicles (UAV). In this context, a relevant scientific challenge is to ensure highly accurate real-time plant disease diagnosis under strict computational and energy constraints of onboard platforms.Aim of the Study. The study is aimed at developing an energy-efficient onboard wheat disease detection system capable of real-time operation on embedded platforms.Materials and Methods. The object of the study was wheat crops under open-field conditions. The system was tested on the computer modules Jetson TX2, NavQ Plus and Raspberry Pi 4. To build the system, there was used a modified YOLO-based architecture integrating lightweight convolutional blocks (GhostConv, MBConv), attention modules (SE, CBAM), and an extended BiFPN feature aggregation structure. Training was based on a dataset of 7,500 annotated images of brown and yellow rust symptoms.Results. The developed model demonstrated high detection quality scores: F1-score up to 0.978 and average IoU of 0.82. Performance achieved 16.8 FPS on Jetson TX2 and 13.6 FPS on NavQ Plus, with energy efficiency up to 2.7 FPS/W.Discussion and Conclusion. The comparative analysis showed that the proposed model outperformed the baseline YOLOv5s according to all key metrics. The proposed architecture demonstrates high accuracy, robustness to noise, and real-time applicability. It can be used to create intelligent crop health control systems and automated plant protection control based on UAV platforms.