Smart Farming: Improving Disease Detection in Pepper Leaves Using AI and Image Processing

Smart farming leverages advanced technologies to enhance crop monitoring, disease detection, and precision agriculture, ensuring higher productivity and sustainability. Early identification of plant diseases is crucial for minimizing yield loss and optimizing intervention strategies. This study presents an AI-driven disease detection system for pepper plants, integrating image processing and machine learning techniques to enable automated crop health assessment. The proposed framework begins with data collection from agricultural fields or institutional repositories. Captured images undergo pre-processing to enhance contrast and reduce noise, followed by feature extraction using Discrete Wavelet Transform with Haar wavelet compression. Extracted features are analyzed using an Artificial Neural Network classifier, employing the back-propagation algorithm to differentiate between healthy and diseased leaves. This AI-powered approach enhances disease identification accuracy, supporting real-time monitoring and smart decision-making for farmers. By integrating machine learning with precision agriculture, the system contributes to efficient farm management, reduced chemical usage, and sustainable farming practices, paving the way for next-generation smart farming solutions.