Enhancing Crop Health, A Novel CNN-SVM Hybrid Model for Litchi Disease Detection

Litchi production has obstacles posed by many illnesses that can profoundly affect both the quantity and quality of the crop. This study introduces an innovative method for predicting diseases by combining Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) into a hybrid model. The model under consideration integrates three convolutional layers, three max-pooling layers, and one fully connected layer. Our model has exceptional performance in accurately identifying several Litchi leaf diseases, as evidenced by extensive experimentation. The CNN-SVM hybrid architecture combines the advantages of both approaches, resulting in a powerful ability to extract features and classify data with reliability. The results of our research enhance the progress of precision agricultural techniques, providing farmers with a powerful tool to diagnose and treat diseases in Litchi orchards at an early stage.