FINE-GRAINED TOMATO DISEASE RECOGNITION BASED ON DEEP CONVOLUTIONAL NETWORK

Abstract

Early diagnosis and accurate identification of fine-grained tomato diseases can control the spread of diseases and insect infections, thus ensuring the healthy development of the tomato industry. In this paper, four lightweight models of Tiny-AlexNet and Mid-AlexNet based on AlexNet and Tiny-VGG16 and Mid-VGG16 based on VGG16 were proposed for 5 kinds of early and late leaf diseases such as tomato powdery mildew. The computation speed of the model is accelerated by reducing the number of neurons in the fully connected layer. In order to avoid degradation in network training, data extension technology is introduced to prevent model overfitting. Among them, the Mid-VGG16 model is significantly better than accurate in early disease recognition, thus verifying the effectiveness of the lightweight model. The proposed model not only improves the accuracy, but also reduces the test time. The results were tested across 20 655 data sets on early and advanced disease. Compared with the traditional model, the average prediction accuracy of the proposed model is improved by about 0.15%, and the detection time is significantly reduced by about 50%. The improved model has strong robustness and high stability. The model can be used to accurately identify early diseases and facilitate real-time detection and prevention of tomato diseases.