🌱 Plant Disease Prediction
Plant Disease Prediction is a deep learning project designed to classify plant diseases from leaf images using Convolutional Neural Networks (CNNs). It provides a simple Streamlit web app where users can upload an image of a leaf and instantly get a prediction about the plant’s health.
This project aims to assist farmers, researchers, and agricultural experts in early disease detection to improve crop yield and reduce losses.
✨ Features
🧠 CNN-based model trained on the PlantVillage Dataset
📂 Supports multiple plant types and disease categories
📸 Image upload & classification via Streamlit
⚡ Real-time prediction with accuracy & confidence scores
📊 Training history visualization (accuracy & loss plots)
💾 Model + class mapping saved for easy deployment
📂 Project Structure Plant_disease_prediction/ │── app/ # Streamlit app files │── model_training_notebook/ # Jupyter notebooks for training │── test_images/ # Sample test images │── Plant-Model/ # Trained models & class indices │ ├── plant_disease_prediction_model.h5 │ ├── class_indices.json │ ├── model-training.py │── README.md │── LICENSE
🚀 Getting Started ✅ Prerequisites
Python 3.7+
TensorFlow / Keras
NumPy, Pandas
Matplotlib, Pillow
Streamlit
⚙️ Setup Instructions
Clone the repository:
git clone https://github.com/shraddhagreddy/Plant_disease_prediction.git cd Plant_disease_prediction
Install dependencies:
pip install -r requirements.txt
Download the PlantVillage Dataset and place it inside your working directory.
🏋️ Model Training
Run the training script:
python Plant-Model/model-training.py
This will:
Train the CNN on the dataset
Save the trained model as plant_disease_prediction_model.h5
Generate class_indices.json for mapping predictions
🌐 Running the Streamlit App
Run the web app with:
streamlit run app/app.py
Upload a leaf image (.jpg, .jpeg, .png)
Get instant disease predictions 🎉
📊 Example Results Training Performance
Model achieves ~92% validation accuracy (update after training).
Sample Prediction
Upload a leaf image → Prediction: Apple Scab 🍏🍂
🔮 Future Improvements
Add Grad-CAM visualizations for explainable AI
Deploy model to cloud (AWS/GCP/Heroku)
Improve accuracy with transfer learning (ResNet, EfficientNet)
Add mobile app integration for farmers
🛠 Tech Stack
Language: Python
Frameworks: TensorFlow, Keras, Streamlit
Data: PlantVillage Dataset
Libraries: NumPy, Pandas, Matplotlib, Pillow
👩💻 Author
Shraddha Reddy 📧 [email protected]
💡 Always open to collaboration & feedback!
📜 License
This project is licensed under the MIT License © 2025 Shraddha Reddy. See the LICENSE file for details.