AI-Powered Image-Based Tomato Leaf Disease Detection

Abstract

The development of Artificial Intelligence over few decades had been incredible, where it converts every segment of the global economy, including agriculture. The traditional approach of the agricultural industry is experiencing a vital revolution. With requiremesnts of better crop yield, AI has been developed as a powerful tool to permit farmers in monitoring and detecting the crop diseases. In addition, farmers can easily identify the crop diseases in early stage by using AI. As traditional plant disease identification includes expertise and high processing time, AI is integrated with image processing with an objective of providing accurate, fast, efficient and inexpensive solution for disease detection. In this paper, novel tomato leaf disease detection is proposed which comprises of four different phases that includes image preprocessing, segmentation, feature extraction and image classification. RGB to grayscale conversion, thresholding, GLCM and random forest classifier are the various algorithms that are used for implementation of the proposed method. The results indicate that the proposed method classifies the diseases with an accuracy of 94.1%.