PLANT DISEASE DETECTION USING DEEP LEARNING AND WEB-BASED APPLICATION

Abstract: Agriculture plays a crucial role in the economic growth of many developing countries, where crop productivity is often threatened by plant diseases. Early and accurate identification of plant diseases is essential to minimize yield loss and ensure sustainable agricultural practices. However, traditional disease detection methods rely heavily on manual inspection and expert knowledge, which are time-consuming, subjective, and not easily accessible to farmers in rural areas. Recent advancements in artificial intelligence, particularly deep learning, offer effective solutions for automated plant disease diagnosis. This paper presents a Plant Disease Detection System based on deep learning techniques for accurate and automated identification of plant diseases from leaf images. The proposed system employs a Convolutional Neural Network (CNN) trained on the Plant Village dataset to classify plant leaves into healthy or diseased categories. The system is implemented as a web-based application using the Flask framework, allowing users to upload plant leaf images and obtain instant disease predictions through a simple and user-friendly interface. Image preprocessing and model inference are handled efficiently to ensure reliable performance. Experimental evaluation demonstrates that the proposed system can accurately identify common plant diseases in a controlled environment, enabling early disease detection and timely preventive measures. The developed solution serves as an effective educational and decision-support platform, highlighting the practical application of artificial intelligence and computer vision in modern agriculture.

Keywords: Plant Disease Detection, Deep Learning, Convolutional Neural Network, Computer Vision, PyTorch, Flask, Agriculture, Image Classification