SSD-YOLO: a lightweight network for rice leaf disease detection

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Abstract
Rice leaf diseases significantly impact yield and quality. Traditional diagnostic methods rely on manual inspection and empirical knowledge, making them subjective and prone to errors. This study proposes an improved YOLOv8-based rice disease detection method (SSD-YOLO) to enhance diagnostic accuracy and efficiency. We introduce the Squeeze-and-Excitation Network (SENet) attention mechanism to optimize the Bottleneck structure of YOLOv8, improving feature extraction capabilities. Additionally, we employ a Dynamic Sample (DySample) lightweight dynamic upsampling module to address high similarity between rice diseases and backgrounds, enhancing sampling accuracy. Furthermore, Shape-aware Intersection over Union (ShapeIoU) Loss replaces the traditional Complete Intersection over Union (CIoU) loss function, boosting model performance in complex environments. We constructed a dataset of 3000 rice leaf disease images for experimental validation of the SSD-YOLO model. Results indicate that SSD-YOLO achieves average detection accuracies of 87.52%, 99.48%, and 98.99% for rice brown spot, rice blast, and bacterial blight respectively—improving upon original YOLOv8 by 11.11%, 1.73%, and 3.81%. The model remains compact at only 6MB while showing significant enhancements in both detection accuracy and speed, providing robust support for timely identification of rice diseases.

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