AI-Powered Crop Disease Detection System

Abstract

Agriculture plays a vital role in sustaining the global economy, and plant health directly influences crop productivity and food security. However, plant diseases often go undetected at early stages due to the limitations of manual inspection, which requires expert knowledge and is prone to human error. To address this challenge, the proposed project introduces an intelligent plant disease detection system based on machine learning and image processing techniques. The system operates by acquiring leaf images, which are then preprocessed through resizing, normalization, and noise reduction to enhance visual quality. Advanced deep learning models such as Convolutional Neural Networks (CNN) are employed to automatically extract relevant patterns and classify leaf images into healthy or diseased categories. A labeled dataset containing multiple plant species and disease variants is used to train and evaluate the model for high accuracy and generalization. Once trained, the model is integrated into a user-friendly interface, allowing farmers or agricultural professionals to upload or capture images using a mobile or web application and receive instant diagnostic results along with suggested remedies. This automated solution significantly reduces dependency on expert consultation, minimizes economic loss due to late detection, and promotes precision agriculture. Moreover, the system can be continuously improved by expanding the dataset to support additional crops and diseases, making it scalable and sustainable for real-world deployment. Overall, this project demonstrates an efficient, low-cost, and technology-driven approach to plant disease management, enabling smarter decision-making and contributing to global agricultural resilience.