Among the most important and widely used crops in the globe is the tomato. The volume of tomatoes varies based on the method of fertilization. The main element affecting crop output in terms of both quality and quantity is leaf disease. Therefore, it is crucial to correctly identify and categorize these afflictions. Early intervention of these infections would lessen their impact on tomato plants and ensure optimum productivity. The various strategies used in plant disease identification are thoroughly reviewed using Machine Learning (ML) and deep learning, both of which are centered on artificial intelligence (AI). Similarly, deep learning has grown significantly in importance for providing improved performance results for identifying plant diseases in the computer vision field. In order to demonstrate the superiority of the deep learning model over the ML model, a comparison of the two techniques' performances and applications in different scientific articles has been made. The deep learning method has the potential to identify leaf diseases from data obtained in order to avoid significant yield reductions.

Agriculture, Plant diseases detection, Machine-learning methods, Deep learning.