Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network

• DOI: 10.3390/electronics11213618
• Publication Date: 2022-11-07T07:43:51Z
• Authors: Gnanavel Sakkarva...; Godfrey Winster S...; Vetri Selvan Murugan; Avulapalli Jayara...; Prabhu Jayagopal; Mahmoud Elsisi
• Keywords: 2. Zero hunger; Convolution Neural Network (CNN); Tomato Leaves Dataset (DLD); Data Augmentation (DA); Deep Learning (DL); Visual Geometry Group (VGG); Residual Network (ResNet); 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease detection. A Convolutional-Neural-Network-based technique is used for disease detection and classification. Inside the model, two convolutional and two pooling layers are used. The results of the experiments show that the proposed model outperformed pre-trained InceptionV3, ResNet 152, and VGG19. The CNN model achieved 98% training accuracy and 88.17% testing accuracy.