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Updated: May 28, 2026
06:41
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
Published on: March 28, 2025
CKM-YOLO11: A Lightweight Maize Foliar Disease Detection Model for Complex Natural Field Environments
Hui Zhu1, Fulin Xiao1, Jinfeng Xiang1
1College of Science, Northeast Forestry University, Harbin 150040, China.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
A new lightweight maize foliar disease detection model, CKM-YOLO11, improves accuracy in complex fields. It enhances early lesion identification, reducing false positives for better crop yield protection.
Area of Science:
- Agricultural Science
- Computer Vision
- Plant Pathology
Background:
- Accurate maize foliar disease detection is crucial for crop monitoring and yield.
- Existing models struggle with visual similarity and background noise in complex field conditions.
- Early disease detection is often hindered by subtle lesions resembling background elements.
Purpose of the Study:
- To develop an improved, lightweight maize foliar disease detection model.
- To enhance the identification of subtle and visually similar disease lesions.
- To improve model robustness in complex natural field environments.
Main Methods:
- Proposed CKM-YOLO11, an enhanced YOLOv11 model incorporating a mixed local channel attention (MLCA) mechanism in the backbone.
- Introduced the C3k2-MLCA module for joint texture, edge, and context modeling.
- Designed the MLCA-HeadLite module in the neck/head to preserve weak lesion signals during feature fusion.
Main Results:
- CKM-YOLO11 achieved an mAP@50 of 81.5% on a challenging dataset.
- Improved mAP@50 by 3.2% and mAP@50-95 by 3.4% compared to the baseline YOLOv11.
- Demonstrated superior background suppression, weak lesion retention, and robustness in complex scenes.
Conclusions:
- The CKM-YOLO11 model offers improved performance for maize foliar disease detection in complex environments.
- The model's lightweight design is suitable for deployment on agricultural edge devices.
- This research provides a valuable reference for developing efficient disease detection systems for agriculture.
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