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UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism

UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism

Shaodan Lin ^1,2, Deyao Huang ^1,3, Libin Wu ^1,3, Zuxin Cheng ^1,4, Dapeng Ye ^5,6, Haiyong Weng ^7,8

Shaodan Lin ^1,2, Deyao Huang ^1,3, Libin Wu ^1,3

¹College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
²School of Information and Intelligent Transportation, Fujian Chuanzheng Communications College, Fuzhou, 350007, China.
³Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China.
⁴College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
⁵College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. ydp@fafu.edu.cn.
⁶Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China. ydp@fafu.edu.cn.
⁷College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. hyweng@fafu.edu.cn.
⁸Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China. hyweng@fafu.edu.cn.

⁰College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Plant Methods +

|

February 5, 2025

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View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces ConvGAM, a novel AI model for detecting rice panicle blast, achieving 91.4% accuracy. This advanced system enhances early disease detection in rice cultivation, crucial for global food security.

Area Of Science

Agricultural Science
Computer Vision
Plant Pathology

Background

Rice blast poses a significant threat to global food security, necessitating precise and timely detection methods.
Current detection methods face challenges with small, complex disease patterns in UAV imagery.

Purpose Of The Study

To introduce ConvGAM, a novel semantic segmentation model for enhanced rice panicle blast detection.
To improve feature extraction and focus on critical disease indicators in UAV-captured rice images.

Main Methods

Utilized ConvNeXt-Large backbone for robust feature extraction.
Integrated Global Attention Mechanism (GAM) to focus on salient image regions.
Employed advanced loss functions, including Focal Tversky Loss, to address data imbalance.

Main Results

Achieved 91.4% overall accuracy, 79% mean IoU, and 82% F1 score.
Demonstrated high consistency with ground truth (correlation coefficients 0.962-0.993).
Effectively handled imbalanced datasets, improving detection of rare and severe disease categories.

Conclusions

ConvGAM shows strong potential for precise rice panicle blast detection and classification.
The model's robustness against data imbalance is a key advantage.
Future work should focus on adaptability to diverse environmental conditions and further optimization.

Keywords:

ConvNeXt FocalTverskyLoss GAM Rice blast Semantic segmentation

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