Detection and Grading of Compost Heap Using UAV and Deep Learning

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non -point source pollution. Utilizing high -resolution imagery acquired through Unmanned Aerial Vehicles (UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non -point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non -point source pollution management strategies, and contributing to the safeguarding of aquatic environments.