🌿 Plant Diseases Prediction using Deep Learning 🔥

🌟 Overview

This project is a Plant Diseases Prediction App built using Streamlit and TensorFlow. The app allows users to upload plant leaf images and get a disease prediction using a pre-trained deep learning model.

🚀 Features

✅ Interactive Streamlit web app 🖥️ ✅ Uses a pre-trained CNN model (trained_cnn_model.keras) 🧠 ✅ Accepts leaf images for disease classification 🌱 ✅ Provides real-time disease detection 🏥

🛠️ Technologies Used

• 🐍 Python
• 🔥 TensorFlow/Keras (Deep Learning Model)
• 🎨 Streamlit (Web App Framework)
• 📊 Pandas, NumPy (Data Processing)
• 📈 Matplotlib, Seaborn (Visualization)

📂 Directory Structure

📁 arpitkadam-plant-diseases-prediction/
├── 📝 main.py                  # Streamlit application
├── 📜 requirements.txt        # Dependencies
├── 🧪 test.ipynb              # Testing and validation script
├── 🎯 train.ipynb             # Model training script
├── 🤖 trained_cnn_model.keras # Pre-trained deep learning model
├── 📄 training_hist.json       # Training history file
├── 🖼️ Visualization_images/    # Training performance images
│   ├── Epochs vs. Training Accuracy.JPG
│   ├── Training Accuracy and Validation Accuracy vs. No. of Epochs.JPG
│   └── Validation Accuracy vs. No. of Epochs.JPG
└── 📂 static/                 # Static assets (if any)

⚡ Installation & Setup

🏗️ 1. Clone the Repository

git clone https://github.com/your-username/arpitkadam-plant-diseases-prediction.git
cd arpitkadam-plant-diseases-prediction

📦 2. Install Dependencies

pip install -r requirements.txt

▶️ 3. Run the Application

streamlit run main.py

🎯 How to Use

1. 📸 Upload an image of the plant leaf.
2. 🎯 Click on Predict Disease.
3. 📢 The app will display the predicted disease along with confidence scores.

🔢 Example Output

Predicted Disease: Powdery Mildew
Confidence: 92.5%

📊 Model Performance

The following graphs illustrate the model's training performance:

🔖 Notes

• 📊 The model is trained on plant disease datasets.
• ⚙️ Predictions depend on the quality and resolution of the uploaded image.
• 🛠️ This is a basic prototype and may require further tuning for better accuracy.

📜 License

This project is open-source and available under the MIT License.

---

🚀 Developed by Arpit Kadam

📧 Contact: 📩 [email protected]