Automatic detection and classification of disease in citrus fruit and leaves using a customized CNN based model
Josephin Shermila P, Akila Victor, S. Oswalt Manoj, E. Anna Devi
Abstract
India's commercial advancement and development depend heavily on agriculture. A common fruit grown in tropical settings is citrus. A professional judgment is required while analyzing an illness because different diseases have slight variations in their symptoms. In order to recognize and classify diseases in citrus fruits and leaves, a customized CNN-based approach that links CNN with LSTM was developed in this research. By using a CNN-based method, it is possible to automatically differentiate from healthier fruits and leaves and those that have diseases such fruit blight, fruit greening, fruit scab, and melanoses. In terms of performance, the proposed approach achieves 96% accuracy, 98% sensitivity, 96% Recall, and an F1-score of 92% for citrus fruit and leave identification and classification and the proposed method was compared with KNN, SVM, and CNN and concluded that the proposed CNN-based model is more accurate and effective at identifying illnesses in citrus fruits and leaves