An Effective and Accurate CNN Model for Detecting Tomato Leaves Diseases

Agriculture is a significant and indispensable principle in humans’ survival, and it rises the sedentary civilization of humans. Besides potatoes, the crop of tomatoes of different forms is commonly cultivated worldwide and utilized almost in all kitchens. However, the quantity and the quality of the crop’s production go down owing to the appearance of different types of diseases. Therefore, there is a need to provide effective tomato diseases detection methods, especially those methods that are based on deep learning algorithms. In this paper, a deep learning model has been developed for detecting tomato leaves diseases. The proposed convolutional neural network (CNN) model is capable of effectively extracting features and accurately classifying a variety of tomato leaves diseases. The experimental results of the comprehensive comparative demonstrate that this developed CNN model achieves a high accuracy of 98% on the dataset of tomato leaf diseases.