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

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Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m.

Yong-Suk Lee^1,2, Maheshkumar Prakash Patil², Jeong Gyu Kim³

¹Department of Food Science and Technology/Institute of Food Science, Pukyong National University, Busan 48513, Republic of Korea.

Plants (Basel, Switzerland)

|March 17, 2025

Summary

This study demonstrates that the YOLOv11m model, after extensive optimization, accurately identifies tomato leaf diseases. This automated disease recognition system enhances crop yield and farm management efficiency.

Keywords:

YOLOv11 hyperparameter optimization one-factor-at-a-time random search tomato leaf disease

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Automated disease recognition in tomato leaves is crucial for improving crop yield and farm management.
Existing methods require efficient and accurate detection systems for various tomato leaf diseases.

Purpose of the Study:

To evaluate the performance of YOLOv11 for automated tomato leaf disease recognition.
To optimize the YOLOv11 model for enhanced accuracy and practical application in agriculture.

Main Methods:

Fine-tuning all accessible YOLOv11 versions on an improved tomato leaf disease dataset (11 classes).
Selecting YOLOv11m for hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm.
Performing random search (RS) with 100 configurations to further refine the model, leading to the C47 model.

Main Results:

The optimized YOLOv11m (C47 model) achieved a fitness score of 0.99268, precision of 0.99190, recall of 0.99348, and mAP@.5 of 0.99262.
The C47 model showed a significant improvement over the initial YOLOv11m model in key performance metrics.
The model demonstrated high accuracy in detecting and identifying 10 different tomato leaf diseases and a healthy class.

Conclusions:

The optimized YOLOv11 model (C47) is highly effective for automated tomato leaf disease recognition.
The developed system is suitable for practical farming applications, aiding in efficient yield management.
This research contributes to the advancement of AI in agriculture for disease detection and crop protection.