Diagnosis of Tomato Plant Diseases Using Pre-Trained Architectures and A Proposed Convolutional Neural Network Model

Abstract

Tomato is one of the most important vegetables in the world. Presence of diseases and pests in the growing area significantly affect the choice of variety in tomato. Early-stage diagnosis plays an important role in determining whether the tomato is subject to effective and economical chemical, mechanical and biological controls, and internal and external quarantine. In this study, deep learning was used to diagnose some diseases in tomatoes. For this purpose, a novel deep CNN-based approach and some Keras models including DenseNet201, InceptionResNetV2, MobileNet, VGG16 architectures were used. Early, middle, and late stages of 18.456 images of Bacterial Spot, Early Blight, Leaf Mold, Septoria Leaf Spot, Target Spot, Mosaic Virus, Yellow Leaf Curl Virus and healthy leaves were examined. The experimental results showed that the custom CNN model produced 99.82% training accuracy. We recommend this model in terms of monitoring and diagnosing of tomato diseases. The results obtained with this study can be used in robotic spraying and harvesting operations.