Multi-Classification of Dragon Fruits Diseases: A Hybrid CNN-SVM Approach

Numerous study techniques will be employed to identify the disease and any specific traits that might harm people’s health. Due to this, the method uses CNNs (Convolutional Neural Networks). This deep learning technique uses input in the form of images to discriminate between them based on various factors. CNNs are most frequently used to analyze visual data. A hybrid CNN-SVM strategy is presented in this study for the multi-classification of dragon fruit illnesses. The suggested method increases the precision and effectiveness of disease diagnosis using the advantages of CNNs and SVMs. The recommended system offers a machine learning-based dragon fruit disease model, in which a user takes a picture of the system for illness diagnosis. Contrasts the numerous systems accuracies of various CNN, Clustering and SVM. To analyze the photographs and provides commentary. With an accuracy of roughly 98.4%, this intelligent system can easily forecast the illnesses that affect dragon fruit.