YOLO-LeafNet: a robust deep learning framework for multispecies plant disease detection with data augmentation

Plant diseases significantly harm crops, resulting in significant economic losses across the globe. In order to reduce the harm that these diseases produce, plant diseases must be diagnosed accurately and timely manner. In this work, a YOLO-LeafNet approach is proposed for detecting diseases from leaf images of four distinct species, namely, grape, bell pepper, corn, and potato. About 8850 leaf images have been acquired for this work from five different publicly available datasets on Kaggle. All the acquired images were pre-processed by applying four different image pre-processing operations. The number of images in the training dataset was tripled for better model performance by applying five different augmentation operations. The augmented dataset was then used to train YoloV5, YoloV8, and the proposed YOLO-LeafNet. The performance of all three models is evaluated in terms of recall, precision, and Mean Average Precision (mAP). The YoloV5 attained a precision of 0.861, recall of 0.868, mAP50 of 0.944, and 0.815 of mAP50-95, and YoloV8 attained 0.977 precision, 0.975 recall, 0.984 of mAP50, and mAP50-95 of 0.915, whereas the proposed the YOLO-LeafNet attained precision of 0.985, recall of 0.980, mAP50 of 0.990, and mAP50-95 of 0.940. The experimental results reveal that the proposed YOLO-LeafNet outperformed YOLOv5 and YOLOv8 in terms of all performance metrics.