ArticleFrontiers in plant science2025
SSD-YOLO: a lightweight network for rice leaf disease detection.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- YOLOv8n-DSLW: A Deployment-Oriented AI-Enabled Vision-Sensing Model for Tiny Strawberry Disease and Pest Detection in Greenhouse Images.Sensors (Basel, Switzerland) · 2026Article
- Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases.Scientific reports · 2026Article
- CED-RTDETR: a contour-aware evidence-guided decoupled network for rice leaf disease detection.Frontiers in plant science · 2026Article
- LightWaveNet: a lightweight wavelet-enhanced high-low-frequency-aware network with multi-stage supervision for rice disease recognition.Frontiers in plant science · 2026Article
- YOLO-SDA: an innovative YOLOv12-derived model with superior performance in recognizing peanut foliar diseases.Frontiers in plant science · 2026Article
- Deep learning enables edge deployment for citrus leaf disease recognition in natural orchard scenes.Frontiers in plant science · 2026Article
- ZamYOLO-maize: a YOLOv8n-based deep learning framework for automated detection and classification of maize leaf diseases in field conditions in Zambia.Frontiers in artificial intelligence · 2026Article
- A lightweight YOLOv11-based model for rice false smut detection under complex field conditions.Frontiers in plant science · 2026Article
- DFSNet: directional feature aggregation and shape-aware supervision for eggplant pest and disease detection.Frontiers in plant science · 2026Article
- A Lightweight Edge-Deployable Framework for Intelligent Rice Disease Monitoring Based on Pruning and Distillation.Sensors (Basel, Switzerland) · 2025Article
- GAME-YOLO: Global Attention and Multi-Scale Enhancement for Low-Visibility UAV Detection with Sub-Pixel Localization.Entropy (Basel, Switzerland) · 2025Article
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4 authors.
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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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