Tomato Leaf Disease Detection Using Machine Learning Algorithms for Automated Crop Health Monitoring

Abstract

Tomato is one of the most widely cultivated horticultural crops, and its productivity is significantly affected by various leaf diseases that reduce yield and quality. Early and accurate detection of tomato leaf diseases is therefore essential for effective crop management and sustainable agriculture. This study presents a machine learning–based approach for automated tomato leaf disease detection using image-based analysis. Leaf images are collected and preprocessed through resizing, noise removal, and normalization to enhance visual quality and consistency. Relevant features such as color, texture, and shape descriptors are extracted to represent disease characteristics effectively. Multiple machine learning algorithms, including Support Vector Machine, Random Forest, and Decision Tree, are trained and evaluated to classify healthy and diseased tomato leaves into different disease categories.
The performance of the models is assessed using standard evaluation metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that machine learning models can accurately identify tomato leaf diseases and significantly reduce the dependency on manual inspection by agricultural experts. The proposed approach provides a cost-effective and efficient solution for early disease diagnosis, enabling timely intervention and minimizing crop losses. This system can be further extended for real-time field deployment and integration with smart farming platforms, supporting precision agriculture and improved food security.