Automatic detection of rice disease in images of various leaf sizes

Abstract To help farmers manage limited resources, rice disease diagnosis must be accurate, timely, and affordable. This study addresses challenges in rice field images, such as environmental variability and differences in rice leaf sizes. The proposed technique combines convolutional neural network object detection with image tiling, using estimated rice leaf width as a reference for image division. An 18‐layer ResNet model was trained using ground truth leaf width values for regression in leaf width estimation. Experiments used a dataset of 4960 images representing 8 rice diseases. The leaf width prediction model achieved a mean absolute percentage error of 11.18% and was used to generate a tiled dataset for training advanced rice disease detection models. The tiling technique was evaluated using YOLOv4, YOLOv8n, YOLOv8l, DINO‐5scale Swin‐L, and Co‐DINO‐5scale Swin‐L models by comparing detection performance on original and tiled datasets. Mean average precision improved significantly: YOLOv4 increased from 87.56% to91.14%, YOLOv8n from 89.80% to 91.70%, and YOLOv8l from 89.80% to 93.20%. More advanced models, such as DINO5scale Swin‐L and Co‐DINO‐5scale Swin‐L, achieved even higher precision, at 93.40% and 94.20%, respectively. In conclusion, the tiling technique improved detection efficiency and addressed object size variability, enhancing rice disease detection accuracy inreal‐world scenarios.