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Light Acquisition

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A multi-scale detection model for tomato leaf diseases with small target detection head.

Hao Sun¹, Xiaofeng Li¹, Xiaofang Li¹

¹Shandong Facility Horticulture Bioengineering Research Center, Weifang University of Science and Technology, Weifang, China.

Frontiers in Plant Science

|October 2, 2025

Summary

A new method, TomatoLeafDet, improves tomato disease detection by effectively processing multi-scale features and small objects. This approach enhances early disease identification, crucial for maintaining tomato yield and quality.

Keywords:

FPN deep learning multi-scale detection serial multi-kernel feature aggregation symmetrical re-calibration aggregation tomato disease detection

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High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay

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Published on: March 10, 2020

Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Tomato diseases significantly reduce crop yield and quality.
Detecting diseases on small tomato leaves presents a major challenge due to scale variations.
Early disease detection is vital for timely intervention and effective crop management.

Purpose of the Study:

To develop an advanced tomato disease detection method addressing multi-scale and small object challenges.
To improve the accuracy and efficiency of identifying diseased tomato leaves.

Main Methods:

Proposed TomatoLeafDet method integrating multi-scale feature processing and small object detection.
Designed Cross Stage Partial -Serial Multi-kernel Feature Aggregation (CSP-SMKFA) for multi-scale feature extraction.
Introduced Symmetrical Re-calibration Aggregation (SRCA) for enhanced feature fusion.
Utilized a Re-Calibration Feature Pyramid Network with a small object detection head.

Main Results:

TomatoLeafDet demonstrated superior performance compared to YOLOv9 and YOLOv10.
Achieved significant improvements in mean Average Precision (mAP50) on the CCMT tomato dataset.
Outperformed baseline models by 4.4%, YOLOv9s by 1.9%, and YOLOv10n by 2.3%.

Conclusions:

The proposed TomatoLeafDet method effectively addresses the challenges of detecting diseased tomato leaves at various scales.
This novel approach offers a promising solution for early and accurate disease identification in tomato cultivation.
The method's enhanced feature processing capabilities contribute to improved detection efficacy and agricultural outcomes.