A Hyperparameters Classification Scheme for Detecting Plant Diseases in Image Processing

Abstract

This research paper proposes a Using image processing techniques, a Hyperparameter classification scheme for plant disease detection was developed. The aim is to enhance the accuracy and reliability of disease identification in plants by combining multiple classification algorithms. The proposed scheme involves preprocessing of captured plant images, extraction of relevant features, feature selection, classifier design, training, and validation. The outputs of multiple classifiers are then fused using fusion techniques to obtain a final disease classification. Experimental results demonstrate the effectiveness of the Hyperparameter scheme in improving plant disease detection accuracy compared to individual classifiers. The proposed approach holds significant potential for automated and reliable plant disease detection in the agricultural sector.