Optimum RBM encoded SVM model with ensemble feature Extractor-based plant disease prediction

Abstract

In agricultural technology, accurate and speedy plant disease identification is essential to maintain the optimum crop quality and output. This research proposed a system that can automatically diagnose diseases in apple fruit and apple trees using machine learning (ML) image processing. Thus, this research offers a novel approach for accurate plant disease prediction by combining an Ensemble Feature Extractor with an Optimum Restricted Boltzmann Machine (RBM) Encoded Support Vector Machine (SVM) model. The model uses RBM-encoded features and SVM classification, and several feature extraction techniques enhance it. The experiments across the PDD271 dataset with 220,592 images and 271 categories demonstrate the model's outstanding classification performance, stressing its potential to develop agricultural technology and enable early disease diagnosis for better crop management. Consequently, with respective values of 98 %, 98 %, 89.7 %, and 97.8 %, the model may give more successful outcomes regarding accuracy, precision, recall, and F1 Score.