🌾 Smart Agriculture Support System 🌦️🦠 A smart solution to assist farmers with fertilizer recommendations, weather condition monitoring, and crop disease detection using machine learning and web-based technologies.

📌 Project Overview This project integrates three main functionalities into one intelligent agricultural support system:

Fertilizer Recommendation: Suggests the most suitable fertilizer based on soil nutrients, crop type, and environmental parameters.

Weather Detection: Retrieves real-time or simulated weather data for analysis.

Disease Identification: Uses machine learning to detect crop diseases from leaf images.

🧠 Features ✅ Fertilizer recommendation based on:

Soil Type

Crop Type

NPK (Nitrogen, Phosphorus, Potassium)

Temperature, Humidity, Moisture

✅ Weather support module (optional real-time integration via API)

✅ Plant disease prediction using image classification (CNN)

✅ Built with Python, Streamlit, Pandas, Scikit-learn, and TensorFlow/Keras

🗂️ Project Structure bash Copy Edit FERTILIZER-RECOMMENDATION/ │ ├── fertilizer_app.py # Streamlit web app for fertilizer recommendation ├── fertilizer_data.csv # Dataset used for training ├── fertilizer_model_train.py # Model training script ├── fertilizer_model.pkl # Trained model ├── requirements.txt # Python dependencies │ DISEASE-DETECTION/ ├── disease_predict.py # Streamlit app for disease prediction ├── model/ # Trained CNN model │ └── plant_disease_model.h5 ├── sample_leaf_images/ # Sample test images │ WEATHER-MODULE/ ├── weather_module.py # Weather prediction or API connector 🚀 Installation Clone the repository

bash Copy Edit git clone https://github.com/your-username/smart-agriculture-support-system.git cd smart-agriculture-support-system/FERTILIZER-RECOMMENDATION Install dependencies

bash Copy Edit pip install -r requirements.txt Run the Fertilizer App

bash Copy Edit streamlit run fertilizer_app.py (Repeat for other apps accordingly.)

🧪 Dataset Sources Fertilizer Dataset: Custom-built or UCI Machine Learning Repository

Disease Dataset: PlantVillage or Kaggle leaf image datasets

Weather Data: OpenWeatherMap API (optional)

📷 Crop Disease Identification (optional extension) Upload a leaf image

Predict if the crop is healthy or has a specific disease

Model used: CNN trained on PlantVillage dataset

✅ Requirements Python 3.8+

streamlit

pandas

scikit-learn

numpy

matplotlib

tensorflow / keras (for disease model)

📌 Future Enhancements Real-time weather API integration

IoT sensor input support

Farmer dashboard with historical analytics

Multi-language support for farmers