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🌾 AgroAlchemy | AI-Powered Agriculture Assistant

AgroAlchemy is a hybrid AI + cloud-based system built to support farmers with smarter crop decisions. It integrates ML models, a Flutter frontend, Firebase backend, and verified agri-data to deliver predictions and recommendations directly to the farmer's mobile device.


🚀 Main Features

🔐 1. Firebase Auth (Login/Signup)

  • Secure login and signup system using Firebase Authentication.
  • Ensures each farmer accesses their personal crop data, prediction history, and suggestions.

🧪 2. Fertilizer Recommender (Decision Tree)

  • Predicts the most suitable fertilizer based on district soil data.
  • Inputs: Nitrogen, Phosphorus, Potassium, pH, Temperature, Rainfall, Soil Color, Crop.
  • Model: Trained Decision Tree using scikit-learn.

🌾 3. Yield & Pesticide Estimator (Decision Tree)

  • Predicts expected crop yield and pesticide requirement based on:
    • State, Season, Rainfall, Fertilizer used, Area.
  • Output: Estimated Yield and Pesticide Quantity.

🦠 4. Plant Disease Detector (Image Classifier - ResNet18)

  • Upload plant leaf images from the app.
  • Output: Predicted disease name with confidence score.
  • Trained on 13+ diseases for Tomato, Potato, Bell Pepper using transfer learning on ResNet18.

🧴 5. Pesticide Recommender (Curated Database)

  • Maps detected disease to recommended pesticide.
  • Verified using authentic agricultural portals and expert sources.

💡 Side Features

📅 Crop History & Calendar

  • Tracks previous predictions and actions by year, crop, and location.
  • Helpful for seasonal planning and comparing historical outcomes.

💬 Chatbot Assistant

  • In-app chatbot helps users interact with the app.
  • Will soon integrate with ML API endpoints for real-time recommendations.

📊 About Page (Hackathon Showcase)

  • Displays model performance (accuracy, loss).
  • Interactive charts and example predictions.

🛠 Tech Stack

Layer Stack
Frontend Flutter (cross-platform mobile app)
Backend Flask API
Database Firebase Firestore (NoSQL)
Auth Firebase Authentication
ML Models Python (scikit-learn, PyTorch, Torchvision)
Deployment Heroku + ML APIs; Firebase handles storage & auth


📦 Installation (Dev Setup)

# Clone the repository
git clone https://github.com/Tejas1234-biradar/PuneAgriHackathon.git
cd PuneAgriHackathon

# Backend Setup
cd server
npm install

# Python ML Setup
cd ../ml-models
pip install -r requirements.txt

# Flutter Frontend Setup
cd ../flutter_app
flutter pub get
flutter run


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