Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network

Abstract

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.

Code and data availability

The paper uses the public Plant Village tomato leaf dataset from Kaggle, but this is a third-party generic dataset, not a paper-specific asset. No author analysis code, trained model checkpoints, or supplementary phenotyping assets are described. The Data Availability Statement says data are available only upon request

No evidence-backed public reproduction asset is currently recorded.