The Potato Disease Classification Project is an innovative solution designed to accurately detect and classify potato diseases using advanced image processing techniques. This repository focuses on leveraging Convolutional Neural Networks (CNNs) to identify three specific categories of potato health: Early Blight, Late Blight, and Healthy Potatoes.
- Disease Classification: Utilizes a CNN model to classify potato images into three distinct categories.
- 100% Accuracy: Achieves full accuracy in detecting diseases from images, ensuring reliable and precise classification.
- Deep Learning Architecture: Incorporates 6 CNN layers and a max pooling layer for enhanced feature extraction and learning.
- Optimized Performance: Utilizes the Adam optimizer for efficient model training and the softmax activation function for probabilistic classification.
The project employs a dataset sourced from Kaggle, which is readily available in an organized format on GitHub, facilitating easy access for users.
- Clone the repository to your local machine.
- Install required libraries for running the CNN model.
- Load the dataset and preprocess the images as needed.
- Train the model using the provided code to achieve the classification task.
- Analyze the classification results for insights into potato health.
For any questions or feedback, please reach out to the project maintainer at mehmodulhaq1040@gmail.com.