Review on Technologies Applied to Classification of Tomato Leaf Virus Diseases

Abstract

Tomato leaf virus diseases present a significant risk to tomato cultivation, leading to substantial financial losses worldwide. Implementing appropriate control measures depends on these diseases being accurately and quickly identified and classified. This article provides an insight into the analysis of the various technologies used to classify tomato leaf virus diseases as well as some similar plant leaf virus disease. The review encompasses both traditional and modern techniques, including image processing, machine learning, and deep learning methods. It explores the use of different imaging techniques, such as visible light RGB, infrared, and hyperspectral imaging, for capturing leaf disease symptoms. Additionally, it emphasizes the growing significance of deep learning models, such as convolutional neural networks, in identifying diseases with extreme precision.  Overall, this study offers insightful information on the technological developments for the categorization of tomato leaf viral illnesses, promoting the creation of efficient disease management techniques.