AI-Based Crop Disease Detection for Smart Agriculture

Abstract

Crop diseases are a major problem in agriculture because late identification can reduce crop quality and production. Manual disease detection is often time-consuming and requires expert knowledge. This research proposes an AI-Based Crop Disease Detection System for Smart Agriculture that uses machine learning and image processing to identify crop diseases from leaf images. The system preprocesses the input image and uses a trained machine learning or deep learning model to classify the disease. A confidence-based decision mechanism can help identify reliable predictions and recommend expert verification when the prediction is uncertain. The proposed system can support early disease detection, reduce manual effort, and help farmers take timely action. This approach has the potential to improve crop monitoring and contribute to efficient and sustainable smart agriculture.