🌱 AgroTech — Smart Agriculture Platform

Empowering farmers with AI-driven crop predictions and data-backed farming insights.

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AgroTech is an intelligent web platform built to modernize Indian agriculture. It combines a Django-powered backend, a curated ML crop predictor, and Google Gemini AI to deliver real-time, natural-language farming advice — putting the power of data science directly in the hands of farmers.

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📌 Table of Contents

• ✨ Features
• 🗂️ Project Structure
• 🛠️ Tech Stack
• 🤖 Google Gemini AI Integration
• ⚙️ Installation
• 🔑 Environment Configuration
• 🚀 Usage
• 🌿 How the Predictor Works
• 📁 Dataset Schema
• 🤝 Contributing
• 📄 License

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✨ Features

Module	Description
🌾 Crop Predictor	ML-powered engine recommending the best crop based on soil & climate inputs
🤖 Gemini AI Advisor	Google Gemini API provides natural-language farming tips, fertilizer advice, and pest control guidance
📊 Smart Datasets	Curated agricultural datasets covering soil nutrients, rainfall, temperature, and humidity
🖥️ Responsive Dashboard	Clean, mobile-friendly UI built with HTML5, SCSS, and vanilla JavaScript
⚙️ Django Backend	Robust server-side logic handling predictions, routing, and data processing
🔒 Secure Architecture	Environment-based configuration, Django ORM, and modular app design

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🗂️ Project Structure

AgroTech/
│
├── Predictor/             # ML model logic — crop prediction engine
│   └── ...                # Saved models (.pkl / .joblib), inference scripts
│
├── backend/               # Django app — views, URLs, models, forms
│   └── ...
│
├── datasets/              # Raw & processed agricultural datasets (.csv)
│   └── ...
│
├── static/                # Static assets — CSS, SCSS, JavaScript, images
│   ├── css/
│   ├── js/
│   └── images/
│
├── templates/             # Django HTML templates — pages and partials
│   └── ...
│
├── manage.py              # Django management entry point
├── requirements.txt       # Python dependencies
├── .gitignore
└── README.md

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🛠️ Tech Stack

Backend

• Python 3.10+
• Django — web framework
• scikit-learn — ML crop predictor
• Pandas & NumPy — data processing
• Google Gemini API — generative AI farming advisor

Frontend

• HTML5, SCSS / CSS3, JavaScript (ES6+)
• Django Templating Engine

Data

• Custom curated CSV datasets (soil nutrients, weather parameters, crop labels)

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🤖 Google Gemini AI Integration

AgroTech uses the Google Gemini API to provide farmers with intelligent, conversational farming advice — going beyond simple predictions.

What Gemini Powers in AgroTech

• 🌿 Fertilizer Recommendations — Based on soil nutrient levels (N, P, K), Gemini suggests organic and chemical fertilizer options in plain language
• 🐛 Pest & Disease Guidance — Farmers can describe symptoms and Gemini returns actionable pest control advice
• 🌦️ Climate-Aware Tips — Seasonal and weather-aware crop care suggestions tailored to local conditions
• 💬 Natural Language Interface — Farmers interact with the platform conversationally, not just through forms

How It Works

Farmer Input (soil data / question)
        │
        ▼
  Django Backend receives request
        │
        ▼
  Prompt constructed with farming context
        │
        ▼
  Google Gemini API call (gemini-pro model)
        │
        ▼
  AI-generated advice returned to UI

Setting Up Your Gemini API Key

1. Visit https://ai.google.dev/ and sign in with your Google account
2. Click "Get API Key" → "Create API Key in new project"
3. Copy the generated key
4. Add it to your .env file as shown in the Environment Configuration section below

⚠️ Never commit your API key to version control. Always use environment variables.

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⚙️ Installation

Prerequisites

• Python 3.10 or higher
• pip (Python package manager)
• Git
• A Google Gemini API key (get one free here)

Step 1 — Clone the Repository

git clone https://github.com/Lucky-939/AgroTech.git
cd AgroTech

Step 2 — Create a Virtual Environment

# Create virtual environment
python -m venv venv

# Activate on Linux / macOS
source venv/bin/activate

# Activate on Windows
venv\Scripts\activate

Step 3 — Install Dependencies

pip install -r requirements.txt

Step 4 — Configure Environment Variables

Create a .env file in the project root (see Environment Configuration below).

Step 5 — Apply Migrations

python manage.py makemigrations
python manage.py migrate

Step 6 — Run the Development Server

python manage.py runserver

Visit: http://127.0.0.1:8000/

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🔑 Environment Configuration

Create a .env file in the root of the project with the following variables:

# Django
SECRET_KEY=your_django_secret_key_here
DEBUG=True
ALLOWED_HOSTS=127.0.0.1,localhost

# Google Gemini AI
GEMINI_API_KEY=your_google_gemini_api_key_here

💡 Tip: Generate a Django secret key using:

python -c "from django.core.management.utils import get_random_secret_key; print(get_random_secret_key())"

Make sure .env is listed in your .gitignore — never push API keys to GitHub.

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🚀 Usage

1. Open your browser and go to http://127.0.0.1:8000/
2. Enter your field parameters — nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall
3. Submit to get an instant ML-based crop recommendation
4. Ask Gemini — use the AI advisor to get fertilizer tips, pest control advice, or farming guidance in natural language
5. Explore the dashboard to understand why a specific crop is recommended for your local conditions

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🌿 How the Predictor Works

User Input (Soil + Climate Parameters)
        │
        ▼
  Feature Preprocessing
  (Normalization / Encoding)
        │
        ▼
  Trained ML Model
  (Random Forest / Decision Tree)
        │
        ▼
  Crop Recommendation Output
  e.g., "Rice", "Wheat", "Cotton"
        │
        ▼
  Gemini AI adds natural-language advice
  e.g., "Apply urea fertilizer before sowing..."

The Predictor/ module contains the pre-trained model, label encoders, and the Python inference script. The Django backend calls this module to serve predictions, then passes context to Gemini for enriched AI guidance.

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📁 Dataset Schema

The datasets/ folder contains curated agricultural data with the following feature columns:

Column	Description	Unit
N	Nitrogen content in soil	mg/kg
P	Phosphorus content in soil	mg/kg
K	Potassium content in soil	mg/kg
temperature	Average ambient temperature	°C
humidity	Relative humidity	%
ph	Soil pH value	0–14 scale
rainfall	Annual rainfall	mm
label	Recommended crop (target variable)	crop name

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🤝 Contributing

Contributions are welcome! Here's how to get started:

# 1. Fork the repository on GitHub

# 2. Clone your fork
git clone https://github.com/YOUR-USERNAME/AgroTech.git

# 3. Create a new feature branch
git checkout -b feature/your-feature-name

# 4. Make your changes and commit
git add .
git commit -m "feat: describe your change here"

# 5. Push to your fork and open a Pull Request
git push origin feature/your-feature-name

Please follow PEP 8 for Python code and include comments where needed.

🐛 Reporting Issues

Found a bug or have a suggestion? Open an issue with:

• A clear description of the problem
• Steps to reproduce it
• Expected vs. actual behaviour
• Screenshots if applicable

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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Made with ❤️ for Indian Farmers — Lucky-939