🌱 Fasal Saathi

🚀 This project is a part of Nexus Spring of Code (NSoC) 2026

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📘 Nexus Spring of Code 2026

This repository is officially participating in Nexus Spring of Code 2026 (NSoC'26).

We welcome contributors from the NSoC program to collaborate and improve this project.

🧑‍💻 For Contributors

• Pick an issue labeled with level1, level2, or level3 or raise an issue
• Ask to be assigned before starting work
• Submit a Pull Request with NSoC'26 in the title
• Follow proper contribution guidelines

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📌 Contribution Rules (NSoC Specific)

• ✅ PR must include NSoC'26 tag
• ✅ Issue must be assigned before PR
• ❌ PR without assignment will be closed
• ❌ Inactive contributors (7 days) may be unassigned

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🏷️ Issue Labels

• level1 — Beginner (level 1)
• level2 — Intermediate (level 2)
• level3 — Advanced (level 3)

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⚠️ Note

This project follows all rules and guidelines defined under the Nexus Spring of Code 2026 program.

Any misuse, spam, or low-quality contributions will not be accepted.

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🌾 Fasal Saathi

Fasal Saathi is a smart agriculture assistance platform built with React (frontend), Python (backend) and Firebase (database/auth). The app delivers crop recommendations, weather-based alerts, soil health analysis, and fertilizer guidance to help farmers make informed decisions.

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🚀 Features

• 🌱 Crop recommendation based on soil profile and regional climate
• ☁️ Real-time weather updates and custom farming alerts
• 🧪 Soil health analysis & nutrient suggestions
• 🌾 Fertilizer and pesticide guidance
• 📊 Responsive and user-friendly dashboard (React)
• 🔐 Authentication & user profiles (Firebase)
• 🌐 Multi-language support (planned / optional)

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🛠️ Tech Stack

• Frontend: React.js (Create React App / Vite)
• Backend: Python ( FastAPI)
• Database: Firebase (Firestore / Realtime DB)
• Auth: Firebase Authentication
• External APIs: Weather API (e.g., OpenWeatherMap), Soil/Agro data APIs
• Deployment: Vercel (frontend), Render (backend- in process)

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📁 Project structure

<<<<<<< HEAD

=======

>>>>>>> upstream/main
Fasal-Saathi/
├── backend/
│ ├── app.py # Flask/FastAPI app entry
│ ├── requirements.txt
│ ├── services/
│ │ ├── weather.py # Weather API wrapper
│ │ └── soil.py # Soil analysis logic / wrapper
│ └── utils/
│ └── ml_models.py # Optional: model for crop recommendation
│
├── frontend/ # React application
│ ├── package.json
│ ├── public/
│ │ └── index.html
│ └── src/
│ ├── App.jsx
│ ├── index.jsx
│ ├── api/
│ │ └── apiClient.js # communicates with backend
│ ├── components/
│ │ ├── Dashboard/
│ │ ├── CropRecommender/
│ │ └── Auth/
│ ├── pages/
│ └── assets/
│
├── firebase/ # firebase rules / config (optional)
│ └── firestore.rules
│
├── scripts/
│ └── deploy.sh
│
├── .env.example
├── README.md
└── LICENSE
<<<<<<< HEAD

=======

upstream/main

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⚙️ Installation & Local setup

Requirements: Node.js (v16+), npm/yarn, Python 3.9+, pip, Firebase CLI (optional).

<<<<<<< HEAD

1) Clone repository

git clone https://github.com/your-username/fasal-saathi.git
cd fasal-saathi

2) Frontend (React)
cd frontend
# Install dependencies
npm install
# Start dev server
npm start
# Build for production
npm run build

3) Backend (Python — Flask example)
cd ../backend
# Create virtual env (optional)
python -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate
pip install -r requirements.txt
# Run app
export FLASK_APP=app.py
export FLASK_ENV=development
# Set environment variables (see .env.example)
flask run


# 4) Firebase

Create a Firebase project.

Enable Firestore (or Realtime DB) and Firebase Auth (Email/Phone).

Add Firebase config to frontend env (see .env.example).

(Optional) Deploy security rules found in firebase/.

🔐 Environment variables (.env.example)
# Backend
=======
## Clone repository

```bash
git clone https://github.com/your-username/fasal-saathi.git
cd fasal-saathi

Frontend (React)

cd frontend

1. Install dependencies

npm install

2. Start dev server

npm start

3. Build for production

npm run build

Backend (Python — Flask example)

cd ../backend

1. Create virtual env (optional)

python -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Run app

export FLASK_APP=app.py
export FLASK_ENV=development

3. Set environment variables (see .env.example)

flask run

Firebase

• Create a Firebase project.

• Enable Firestore (or Realtime DB) and Firebase Auth (Email/Phone).

• Add Firebase config to frontend env (see .env.example).

• (Optional) Deploy security rules found in firebase/.

🔐 Environment variables (.env.example)

Backend

>>>>>>> upstream/main
WEATHER_API_KEY=your_weather_api_key
SOIL_API_KEY=your_soil_api_key
FIREBASE_ADMIN_CRED=/path/to/serviceAccountKey.json
BACKEND_PORT=5000
<<<<<<< HEAD

# Frontend
=======

Frontend

>>>>>>> upstream/main
REACT_APP_FIREBASE_API_KEY=xxxxxxxxxxxx
REACT_APP_FIREBASE_AUTH_DOMAIN=your-app.firebaseapp.com
REACT_APP_FIREBASE_PROJECT_ID=your-app
REACT_APP_BACKEND_URL=http://localhost:5000
<<<<<<< HEAD

🧩 API Endpoints (examples)

Backend (Flask)

GET /api/weather?lat={lat}&lon={lon} — returns current weather + forecast

POST /api/soil/analyze — send soil params (pH, NPK) to get recommendations

POST /api/crop/recommend — returns recommended crops for given soil & climate

(Document exact request/response schemas in docs/ or OpenAPI spec.)

🧪 Testing

Frontend: use React Testing Library / Jest

Backend: pytest / unittest

Add CI with GitHub Actions for linting + tests + deploy

🎯 Objective

Provide farmers with a lightweight, region-aware digital assistant that reduces risk, improves yields, and encourages sustainable decisions through actionable insights.

🔮 Future scope & ideas

On-device offline support / PWA for low-connectivity regions

Integrate satellite / remote sensing for crop stress detection

SMS / WhatsApp alerts for farmers without smartphones

Integrate local market price data for crop sale recommendations

Train ML models using local farm historical data for precision recommendations
=======

☆ API Endpoints (examples)

Backend (Flask)

• GET /api/weather?lat={lat}&lon={lon} — returns current weather + forecast

• POST /api/soil/analyze — send soil params (pH, NPK) to get recommendations

• POST /api/crop/recommend — returns recommended crops for given soil & climate

(Document exact request/response schemas in docs/ or OpenAPI spec.)

☆ Testing

• Frontend: use React Testing Library / Jest

• Backend: pytest / unittest

• Add CI with GitHub Actions for linting + tests + deploy

☆ Objective

Provide farmers with a lightweight, region-aware digital assistant that reduces risk, improves yields, and encourages sustainable decisions through actionable insights.

☆ Future scope & ideas

• On-device offline support / PWA for low-connectivity regions

• Integrate satellite / remote sensing for crop stress detection

• SMS / WhatsApp alerts for farmers without smartphones

• Integrate local market price data for crop sale recommendations

• Train ML models using local farm historical data for precision recommendations

upstream/main