@article{Meng2026, 
author = {Qingtao Meng and Sang-Hyun Lee},
title = {Lightweight YOLOv5 with ShuffleNetV2 for Rice Disease Detection in Edge Computing},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {1},
pages = {1-15},
keywords = {Lightweight object detection, YOLOv5-V2, ShuffleNet V2, edge computing, rice disease detection},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.069970},
doi = {10.32604/cmc.2025.069970},
abstract = {This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone, thereby resulting in both model compression and improved inference speed. Additionally, YOLOv5-P, based on PP-PicoDet, was configured as a comparative model to quantitatively evaluate performance. Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance, with an mAP 0.5 of 89.6%, mAP 0.5–0.95 of 66.7%, precision of 91.3%, and recall of 85.6%, while maintaining a lightweight model size of 6.45 MB. In contrast, YOLOv5-P exhibited a smaller model size of 4.03 MB, but showed lower performance with an mAP 0.5 of 70.3%, mAP 0.5–0.95 of 35.2%, precision of 62.3%, and recall of 74.1%. This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.}
}