Potato Leafs Disease Classification using CNN
This project utilizes Convolutional Neural Networks (CNN) to accurately classify potato leaf diseases. CNNs are a deep learning technique highly effective for image classification tasks, and this project showcases their application in the field of agriculture. The model is trained to recognize different diseases in potato plants by analyzing images of the leaves. The classification can aid farmers in identifying and taking action against diseases before they spread, ensuring better crop management and higher yields.
Project Objective
The objective of this project is to build a CNN model capable of classifying potato leaf diseases, enabling automated detection and assistance for agricultural practices. The CNN model uses images of leaves to train and predict disease types with high accuracy.
Key Features:
- Convolutional Neural Network for image classification.
- Accurate disease prediction from leaf images.
- Training with publicly available datasets for potato leaf diseases.
- Integration with real-time farming applications for disease management.
Images from the Project
