This project aims to build a plant disease classification model using a Convolutional Neural Network (CNN), powered by TensorFlow and Keras. The goal is to classify images of plants into one of 38 categories, identifying various diseases and plant types. The dataset used is the PlantVillage dataset from Kaggle.

• Reproducibility: Random seeds for TensorFlow, Numpy, and Python ensure experiments are reproducible.
• Data Curation: The project utilizes the Kaggle API to download and manage the PlantVillage dataset, which includes three types of images: segmented, color, and grayscale.
• Data Preprocessing: Essential preprocessing steps, such as image resizing, normalization, and augmentation, are performed to optimize the dataset for model training.
• Model Building: A simple yet effective CNN architecture with convolutional layers, max-pooling layers, and dense layers for classification.
• Training and Evaluation: The model is trained and evaluated on the dataset, with insights on accuracy and loss.
• Prediction System: A function to predict plant disease from new images using the trained model.
• Model Saving: The trained model is saved for later use in production or further research.

1. Clone this repository to your local machine:

   git clone https://github.com/your-username/plant-disease-classification.git
   cd plant-disease-classification

2. Install the required dependencies:

   pip install -r requirements.txt

3. Ensure you have the Kaggle API set up for downloading the dataset. You will need a Kaggle account and an API key:

   • Go to Kaggle → Account → Create API Token.
   • Place the kaggle.json file in the root directory of this project.

4. Download the dataset using the Kaggle API:

   kaggle datasets download -d emmarex/plantdisease

The dataset contains images of plants categorized into 38 classes (plant types or diseases). The images are grouped into three categories:

• Segmented: Images with clear plant features isolated.
• Color: Natural colored images of plants.
• Grayscale: Black and white images.

Random seeds for Python, Numpy, and TensorFlow are set to ensure reproducibility of results.

• Images are resized and normalized.
• Data augmentation (e.g., rescaling) is applied to improve model generalization.
• The dataset is split into training (80%) and validation (20%) sets.

• A simple CNN with two convolutional layers followed by max-pooling layers is built.
• The model ends with two dense layers for multi-class classification.
• The Adam optimizer and categorical cross-entropy loss function are used for training.

• The model is trained for 5 epochs.
• Batches of 32 images are used with a target size of 224x224 pixels.

• The trained model is evaluated on the validation dataset.
• Accuracy and loss plots are generated to track model performance.

• The predict_image_class function allows you to predict plant diseases from new images.
• Class indices are saved in a JSON file for easy reference.

• The trained model is saved as plant_disease_prediction_model.h5 for future use.

• Data Augmentation: Improves model performance by introducing variability in the training data.
• Overfitting Prevention: Monitoring loss curves helps detect overfitting. Regularization techniques may be necessary if overfitting is detected.
• Model Evaluation: Both accuracy and loss are crucial for understanding how well the model generalizes to unseen data.
• Model Saving: Saves time by allowing you to use the model later without retraining.

• Hyperparameter Tuning: Try different architectures and hyperparameters (e.g., learning rate, number of layers) for better performance.
• Deployment: Host the model using a web service to allow users to upload images and get predictions in real-time.
• Advanced Data Augmentation: Experiment with more complex augmentation techniques (e.g., rotation, flipping) to improve model robustness.

• Kaggle PlantVillage Dataset
• TensorFlow Documentation
• Keras Documentation

Feel free to fork this repository and create pull requests. Contributions are always welcome!

🌱 Happy coding and best of luck in plant disease classification! 🌿