R-UAV-Net: Enhanced YOLOv4 With Graph-Semantic Compression for Transformative UAV Sensing in Paddy Agronomy

Abstract

Common leaf diseases pose severe problems to the agricultural industry, particularly for paddy rice, a staple crop consumed worldwide, making early detection and rapid prevention crucial for maintaining both quality and yield. This research dwells on the object detection farmwork for identifying and localising paddy leaf diseases. Future-tech Unmanned Aerial Vehicles (UAVs) offer benefits such as reduced deployment costs, increased availability, enhanced operability, and improved geographical and temporal resolution. You Only Look Once (YOLO) models excel in disease part detection but require excessive computing. A severe challenge of UAV sensing is the resource-efficient collection, transmission and disease detection from this high-resolution ground data. This research addresses these issues by introducing a Graph-inspired encoder-decoder Semantic Compression (G-SC) coupled with enhanced YOLOv4 architecture for disease detection in paddy agronomy. The proposed R-UAV-Net is an improved YOLOv4 architecture incorporating various spatial and channel feature extraction blocks with attention mechanisms for revolutionizing precision farming. R-UAV-Net outperformed state-of-the-art (SOTA) techniques, showing a 0.69% improvement in mean average precision (mAP) and a 0.12 increase in F1 score over the best-performing leaf detection model.