Tomato leaf disease detection and classification based on deep convolutional neural networks

Abstract

Tomato is one of the major crops in India. The production of tomatoes is rigorously affected by several types of diseases. Therefore, initial detection of disease is vital for the quality and quantity of tomatoes. It is significant to monitor crop growth. There are many diseases that mostly affect the plant leaves. This paper adopts a convolutional neural network (CNN) model to detect and identify diseases using the images of tomato leaves. The proposed CNN model comprises four convolutions and four max pooling layers, which are followed by fully connected layers. The performance of the proposed method is assessed by performing experiments on a well-known PlantVillage dataset. There are nine diseases and one healthy class for tomato crop in the dataset. The overall accuracy of the proposed method is obtained as 96.26%. It is compared with some fine-tuned pre-trained CNN models InceptionResNetV2 and InceptionV3. The results illustrate that the proposed method outperforms all the methods based on fine-tuned models.