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Established web app employs Python's Flask Framework for frontend structure, linking with a backend ML model to classify disease types in potato plants based on leaf images and the application of Convolutional Neural Networks.
Potato Disease Classification done by using deep-learning and for the sake of knowing various diseases caused to Potato plant and for quick remedial action. Link of the website 👇
This project aims to develop an automated potato disease classification system using deep learning. By leveraging a Convolutional Neural Network (CNN), the model classifies high-resolution images of potato leaves into different categories, including healthy and diseased plants (early blight, late blight). The system is deployed using Flask and proc
Developed a deep learning model using TensorFlow and Convolutional Neural Networks to classify disease images of potato plants, including early blight, late blight, and overall plant health in agriculture. Model achieved an impressive accuracy of 97.8%, empowering farmers with precise treatment applications to enhance crop yield and quality.
Potato Disease Classification using TensorFlow is a project designed to identify three types of potato plant health: Early Blight, Healthy, and Late Blight. This machine learning model employs convolutional neural networks (CNNs) to analyze images, aiding farmers in early disease detection and crop protection.