Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m.

Abstract

The automated recognition of disease in tomato leaves can greatly enhance yield and allow farmers to manage challenges more efficiently. This study investigates the performance of YOLOv11 for tomato leaf disease recognition. All accessible versions of YOLOv11 were first fine-tuned on an improved tomato leaf disease dataset consisting of a healthy class and 10 disease classes. YOLOv11m was selected for further hyperparameter optimization based on its evaluation metrics. It achieved a fitness score of 0.98885, with a precision of 0.99104, a recall of 0.98597, and a mAP@.5 of 0.99197. This model underwent rigorous hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm, with a focus on essential parameters such as batch size, learning rate, optimizer, weight decay, momentum, dropout, and epochs. Subsequently, random search (RS) with 100 configurations was performed based on the results of OFAT. Among them, the C47 model demonstrated a fitness score of 0.99268 (a 0.39% improvement), with a precision of 0.99190 (0.09%), a recall of 0.99348 (0.76%), and a mAP@.5 of 0.99262 (0.07%). The results suggest that the final model works efficiently and is capable of accurately detecting and identifying tomato leaf diseases, making it suitable for practical farming applications.

Code and data availability

The paper's tomato leaf disease images derive from the public Kaggle 'Tomato Disease Multiple Sources' dataset, explicitly cited as openly available in the Data Availability Statement. The supplement (Data S1) contains the random-search performance configurations from the authors' hyperparameter optimization analysis.