YOLO-Ash: Advanced YOLO11 Modifications for Ash Gourd Leaf Diseases Detection

Abstract

The detection and classification of plant diseases are critical for improving crop yields and ensuring food security. This study focuses on developing an enhanced object detection model tailored for Ash Gourd leaf diseases, a crop prone to significant yield losses due to common diseases such as Aphid infestation, Downy Mildew, Leaf Curl, and Leaf Miner. We proposed a customized YOLOv11 architecture with significant modifications, including the integration of the Convolutional Block Attention Module (CBAM), an Enhanced Spatial Pyramid Pooling-Fast (SPPF) module, the Hard-Swish activation function, and an optimized Non-Maximum Suppression (NMS) technique. These enhancements aimed to improve the model's ability to extract disease-relevant features while maintaining computational efficiency. The model was trained on a newly curated dataset of Ash Gourd leaf images, captured under real-world conditions, and classified into five categories: Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner. Experimental results demonstrate that the proposed model achieves superior performance compared to baseline architectures, with a precision of 89.3%, recall of 88.8%, and mAP@0.5 of 89.3%. Incorporating attention mechanisms and feature fusion techniques significantly improved detection accuracy, particularly for subtle and overlapping disease patterns. This study highlights the potential of the customized YOLOv11 model as a robust and efficient tool for precision agriculture applications.