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Potato Disease Prediction

Project Overview

The goal of the Potato Disease Prediction project is to predict whether a potato leaf is affected by late blight, early blight, or if it is healthy. The project utilizes machine learning techniques, specifically Convolutional Neural Networks (CNN), to analyze images of potato leaves and provide accurate disease predictions.

Dataset

The dataset used in this project is sourced from Kaggle. It includes images of potato leaves with labels indicating whether they are affected by late blight, early blight, or are healthy.

Tools Used

  • Python
  • TensorFlow
  • Matplotlib

Process

  1. Data Collection: The dataset is collected from Kaggle, consisting of images of potato leaves with corresponding disease labels.

  2. Data Cleaning: Preprocessing steps are performed to clean and prepare the data for training.

  3. Data Augmentation: To enhance the model's ability to generalize, data augmentation techniques are applied to artificially increase the diversity of the training dataset.

  4. Data Splitting: The dataset is divided into training, testing, and validation sets to evaluate the model's performance.

  5. Model Training: A Convolutional Neural Network (CNN) is employed to train the model on the prepared dataset.

Installation

To run the Potato Disease Prediction project, follow these steps:

  1. Clone the repository: 
    git clone https://github.com/your-username/potato-disease-prediction.git
  2. Install dependencies:

pip install Tensorflow , Matplotlib

  1. Run the potato-disease-classification-model.ipynb file