An efficient tomato diseases detection and classification methodology using CNN Deep Learning Network

Abstract

In this paper, an efficient methodology for detecting and classifying tomato diseases using a Convolutional Neural Network (CNN) deep learning network is presented as an efficient aided tool to classify different tomato diseases based on their leaf appearence, where plant diseases and insects are considered as a main challenges for farmers to overcome. The proposed methodology structure is based on 20 layers using convolution, Maxpooling, Batch normalization and ReLU process as main operations in the adopted architecture. The obtained results using Plant Village database show that our proposed methodology outclasses the best recent methods of tomato diseases detection and classification with scores of 97.3% 96.8% 97.0% and 97.5% for precision, Recall, F1-Score and accuracy coefficient criteria respectively. Our methodology of tomato diseases detection and classification is found as effective, accurate and aided diagnostic tool that aims to aid farmers to make a precise plant treatments and enhancing productivity while promoting environmental sustainability.