Optimizing YOLOv11 for Rice Disease Detection: Integrating RepViT Backbone, BiFPN, and CBAM Attention
Sang-Hyun Lee*, Qingtao Meng
Department of Computer Engineering, Honam University, Gwangsangu, Gwangju, Republic of Korea
* Corresponding Author: Sang-Hyun Lee. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.077207
Received 04 December 2025; Accepted 17 February 2026; Published online 09 March 2026
Abstract
Accurate and timely detection of rice leaf diseases is critical for ensuring global food security and maximizing agricultural yields. However, existing deep learning methods often struggle to balance the high accuracy required for detecting multi-scale lesions in complex field environments with the computational efficiency necessary for edge device deployment. This paper proposes You Only Look Once for Lightweight Detection (YOLOv11-LD), a lightweight object detection model for multi-scale rice leaf disease detection in real paddy field environments. The model is built on YOLOv11n and integrates a Re-parameterized Vision Transformer (RepViT) backbone, a Bidirectional Feature Pyramid Network (BiFPN) based neck, and a Convolutional Block Attention Module (CBAM) to enhance multi-scale feature representation to enhance multi-scale feature representation while maintaining a lightweight architecture suitable for edge deployment. A dataset of 3234 images captured in actual rice paddies was constructed, containing three major rice leaf diseases: bacterial blight, rice blast, and brown spot. and was split into 2241 training images and 993 validation images. Ablation experiments show that the full YOLOv11-LD configuration achieves 95.2% mAP_0.5 with 7.8 Giga Floating-Point Operations (GFLOPs) and 3.5M parameters, outperforming the baseline YOLOv11n (91.4% mAP_0.5) under the same input resolution of 640 × 640. Additional comparisons with Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot MultiBox Detector (SSD), YOLOv5n, YOLOv8n, and YOLOv11n further confirm that YOLOv11-LD provides the best overall trade-off between detection accuracy and computational efficiency. These results demonstrate that YOLOv11-LD offers superior operational efficiency suitable for resource-constrained smart rice disease monitoring systems.
Keywords
Rice leaf disease detection; lightweight object detection; YOLOv11-LD; RepViT backbone; BiFPN
Open Access
Login
Register
Submit a Paper
Propose a Special lssue
Download PDF
Supplementary Material File
Downloads
Citation Tools
