A Comprehensive Review on Deep Learning and Machine Learning Approaches for Tomato Leaf Disease Identification

Farming is India's primary economic sector. More than 58% of rural income is generated by the agricultural sector. One of the most extensively produced food crops in India is tomatoes. So, as tomato plants grow less resistant to disease, early diagnosis is more important than ever. The plant's output will drop if proper maintenance is neglected. Tomato leaves are susceptible to a wide range of leaf-borne illnesses. When insects destroy crops and plant life, it slows a country's progress. To find and differentiate signs of plant sickness, experts often inspect the plants without the use of a microscope. The problem is that this approach is often inefficient, expensive, and time-consuming. A comprehensive bibliographic review of Deep Learning (DL) and Machine Learning (ML) methods for Tomato leaf disease identification is presented in this research. It is believed that this work will serve as a helpful tool for the agricultural disease research community using technology for early identification and control of tomato leaf diseases.