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Updated: Jun 20, 2026
15:25
Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
Published on: March 16, 2010
Robust multi-target multi-scale tomato leaf disease detection for precision agriculture applications.
Jun-Zhang Pan1, Yang Xie2, Shuai-Yang Zhao1
1College of Information Engineering, Tarim University, Alaer, China.
Frontiers in Plant Science
|June 19, 2026
Summary
This study introduces an improved YOLOv8s model for accurate and fast detection of multiple tomato leaf diseases. The enhanced model achieves high precision and recall, offering a robust solution for agricultural applications.
Area of Science:
- Agricultural Science
- Computer Vision
- Machine Learning
Background:
- Tomato foliar diseases significantly impact global crop yield and economic value.
- Current disease detection methods lack accuracy and speed for complex, multi-target scenarios.
Purpose of the Study:
- To develop a high-precision, fast identification model for multi-target, multi-scale tomato leaf diseases.
- To enhance the robustness and diagnostic capabilities for automated agricultural disease detection.
Main Methods:
- A novel improved YOLOv8s model incorporating a Convolutional Block Attention Module (CBAM) was developed.
- A comprehensive multi-target, multi-scale tomato disease dataset was created using public data and augmentation.
- Transfer learning was applied to improve model convergence and generalization.
Main Results:
- The improved YOLOv8s-CBAM model achieved 96.9% precision, 97.3% recall, 97.0% F1 score, and 99.1% mAP@0.5.
- Performance metrics showed significant improvements over the original YOLOv8s model.
- Model size was reduced to 24.8 MB, balancing accuracy with a lightweight design.
Conclusions:
- The YOLOv8s-CBAM model demonstrates superior feature extraction and localization stability for complex disease identification.
- This method provides an effective technical solution for automated detection in challenging agricultural environments.
- The study highlights the potential of advanced deep learning models in precision agriculture.
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