Machine Learning Based Tomato Leaf Disease Detection Technique

Tomato is the most liberal & significant crops found in the world. Crop production of tomato can be seriously affected due to diseases detected in plant and improper treatments. Climate, weather, natural disasters, the diseases in plant are other calamities that have a significant impact on agricultural productivity and causes economic loss. In this context, this study presents a comparative study of the leaf disease diagnosis in tomato plant using machine learning (ML) techniques. A review of different existing models is done in order to identify which model works better and provides much higher accuracy. A InceptionV3 based ML model has been proposed in order to diagnose leaf disorders in tomatoes at an early stage and further protect the plant from other diseases, the proposed method offers classification accuracy of 81%.