Classification of Diseases in Chili Plants Using the SVM Method: Development and Implementation

Chili peppers (Capsicum annuum) are a significant agricultural commodity with considerable economic value. However, their productivity is susceptible to diseases and environmental factors. To mitigate these challenges, current research is focusing on the early detection and management of diseases. One promising approach is the use of Support Vector Machine (SVM) techniques to classify chili diseases. This study proposes the development of an SVM model enhanced with data augmentation techniques to optimize existing datasets. The methodology follows the CRISP-DM framework, which comprises six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Evaluation results demonstrated an SVM classification accuracy of 92.13%, indicating the model's effectiveness in distinguishing between healthy leaves, Gemini virus, and mosaic curl. The deployment phase includes a user-friendly web application that allows users to upload images of chili leaves to receive instant diagnoses and treatment recommendations. This method is expected to improve understanding of chili plant health and support more informed decision- making in agricultural management.