CABBAGE DISEASE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Cabbage is a widely cultivated vegetable crop susceptible to various diseases, which can significantly reduce crop yield and quality. To effectively monitor these diseases, accurate and timely classification is essential. This study proposes cabbage plant disease classification using convolutional neural networks (CNN). The proposed method involves preprocessing input images, extracting relevant features using a developed CNN model, and accurately classifying diseases affecting cabbage plants. The cabbage leaf image dataset was collected from Sidama region Hawassa zuriya deneba and east shewa zone of the Oromia region dugda woreda meki town cabbage production areas in Ethiopia. The performance of the proposed method is evaluated on 5700 datasets of cabbage plant images with five types of diseases, and one healthy cabbage class such as Alternaria leaf spot, anthracnose, black rot, cabbageworm, downy mildew, and healthy cabbage. We used the data augmentation method to expand the training dataset. 70% of the dataset was used for training, and the remaining 30% was used for testing and validation. We used existing research to compare the proposed model and obtained better results. The proposed model is evaluated using certain metrics such as precision, recall, F1-score, and accuracy. 96.80% of precision, 96.65% of recall, and 96.67% of F1-score. The proposed model achieved 99.53% training accuracy and 98.51% test accuracy for classified experiment results. The results show that the proposed approach can provide accurate and efficient disease diagnosis for cabbage plants, enabling timely and targeted intervention to prevent crop loss