Potato Disease Classification with CNN #867
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👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. |
UppuluriKalyani
approved these changes
Nov 10, 2024
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🎉🎉 Thank you for your contribution! Your PR #867 has been merged! 🎉🎉 |
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Related Issues or bug
The accurate and timely identification of potato diseases is essential for agricultural productivity and food security. Traditional methods are often manual, slow, and prone to human error. This project aims to create an automated, high-accuracy model for detecting various potato diseases from leaf images. By applying deep learning techniques, this model assists farmers and agricultural experts in efficiently identifying diseases, leading to better crop management and reduced losses.
Fixes: #863
Proposed Changes
This project focuses on using Convolutional Neural Networks (CNNs) to classify potato diseases based on leaf images. With an architecture optimized for image classification tasks, the model uses data augmentation, resizing, and normalization techniques to improve training and prediction accuracy. The CNN model, designed in TensorFlow, identifies and categorizes images into predefined classes of potato diseases, enhancing agricultural diagnostics through deep learning techniques.