REAL-TIME TOMATO LEAF DISEASE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Like most plant diseases, tomato leaf disease has physical symptoms. The currently accepted technique is for a trained plant pathologist to identify the condition through visual inspection of affected plant leaves and stems. But due to the manual process, the disease identification time and accuracy have always been questionable facts, making the problem a great application area for Computer-aided diagnostic techniques. Due to the durability and cutting-edge performance, the convolutional neural network (CNN) has been proposed in this study for the real-time classification of tomato leaf disease. The picture data used in this study for tomato leaves came from Plant Village databases. The models employed in this study were trained and tested using 16011 images from original and augmented datasets. The results are then applied to create a real-time system for mobile applications. The initial adjustment with the CNN architecture is proposed to get higher accuracy. A step-by-step systematic approach to parameter tuning is also proposed to improve the system’s performance. The proposed methods show maximum prediction accuracies of 98.00%, and 99.04% are achieved.