TOMATO PLANT DISEASE DETECTION AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES: A COMPREHENSIVE REVIEW

Abstract

Tomatoes (Solanum lycopersicum) are critical to global agriculture, but their cultivation is threatened by diseases caused by fungi, bacteria, and viruses. Traditional disease detection methods are often manual and inefficient, resulting in delayed diagnosis and increased crop loss. Recent advances in machine learning (ML) and deep learning (DL) technologies have revolutionized disease detection by automating this process. These technologies utilize extensive image datasets and environmental data to train algorithms that are capable of identifying disease symptoms with high accuracy. This review explores various ML and DL techniques for tomato disease detection, including support vector machines, artificial neural networks, and convolution neural networks. This review also examines the integration of these technologies into practical tools, such as mobile applications for realtime diagnostics. The findings indicate that deep learning models offer superior accuracy compared to traditional methods, and that incorporating environmental data enhances prediction reliability. Challenges, such as data scarcity, real-world variability, and the need for userfriendly applications, remain. Future research should focus on integrating Internet of Things (IoT) technologies for real-time monitoring, fostering collaborative datasharing, and developing intuitive tools for farmers. By harnessing ML and DL, the agricultural sector could advance disease management, improve crop productivity, and promote sustainability.