Hybrid Feature Approach for Plant Disease Detection and Classification using Machine Learning

Abstract

Plant diseases identification and classification is a salient task in the agriculture field and has significant impact on crop quantity and quality. Early detection of plant diseases can contribute to reduce losses and increase crop productivity. Accurate identification and categorization of plant diseases was necessary for enhancing crop cultivation and increased crop production yield, for that an image-processing approach could be used. The proposed hybrid feature extraction technology, which integrates Discrete Wavelet Transform decomposition and Grey Level Co-Occurrence Matrices feature extraction with Support Vector Machine classifier could identify and categorize plant diseases to an extent of 95.16 to 98.38% and gave better performance as compared to another model.