🌾 FarmAiHelp – AI Crop Planner for Indian Farmers

📖 Introduction

FarmAiHelp is an AI-powered decision-support system designed to empower Indian farmers with actionable insights for crop planning.

Every farming season, farmers face a critical decision: which crop to plant. Choosing the wrong crop can lead to low yields, wasted resources, and financial losses. FarmAiHelp tackles this challenge by combining agricultural knowledge, market intelligence, and AI to recommend the most suitable crop for a farmer’s unique conditions.

By analyzing factors such as:

• 🌍 Land size
• 💧 Water availability
• 🌦️ Local weather patterns
• 📊 Current market prices

FarmAiHelp suggests the best crop to cultivate and provides an estimated return on investment (ROI), enabling farmers to maximize profit while using resources sustainably.

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🎯 Project Goals

• ✅ Support Indian farmers with AI-driven, accessible tools.
• ✅ Optimize agricultural decision-making based on local conditions.
• ✅ Improve farmer incomes through better ROI forecasting.
• ✅ Promote sustainability by matching crop choices with water and climate realities.
• ✅ Bridge the technology gap with a user-friendly web-based platform.

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📑 Table of Contents

• Introduction
• Project Goals
• Features
• System Architecture
• Tech Stack
• Installation
• Usage
• Configuration
• Example Workflow
• Future Enhancements
• Troubleshooting
• Contributors
• License

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✨ Features

• 📊 AI-Powered Crop Recommendations – Personalized crop suggestions tailored to farmer inputs.
• 🌦️ Weather-Aware Planning – Accounts for seasonal climate and rainfall patterns.
• 💧 Resource Optimization – Matches crops with available water and land size.
• 💹 ROI Estimation – Provides projected financial outcomes for informed decision-making.
• 🌍 Localized Insights – Focused on Indian agriculture and market dynamics.
• 💻 Web-Based Interface – Simple, intuitive, and accessible to all farmers.

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🏗 System Architecture

flowchart TD
    A[Farmer Inputs] -->|Land Size, Water, Weather, Market Prices| B[Flask Backend]
    B --> C[AI Model - Crop Recommendation Engine]
    C --> D[AI Crop Recommender]
    D --> E[Frontend - TypeScript + CSS]
    E --> F[Farmer Output: Best Crop + ROI]

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• Farmer Inputs: Land size, water availability, local weather, market prices.
• Backend (Flask): Handles data preprocessing, model execution, and API endpoints.
• AI Model: Predicts suitable crops based on input parameters.
• AI Crop Recommendations: Estimates financial returns based on crops recommended.
• Frontend (TypeScript + CSS): Displays results in an accessible web UI.

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🛠 Tech Stack

• Backend: Python, Flask
• Frontend: TypeScript, CSS
• AI/ML Models: Python-based recommendation system
• Deployment: Web service (local or cloud-hosted)

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⚙️ Installation

1. Clone the repository

git clone https://github.com/AakashMutum/Team-Obsidian---FarmAiHelp.git
cd Team-Obsidian---FarmAiHelp

2. Create & activate a virtual environment

python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

3. Install dependencies

pip install -r requirements.txt

4. Run the Flask server

python app.py

The app will start at: http://127.0.0.1:5000/

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🚀 Usage

1. Open the web app in your browser.
2. Enter required details:
   • Land size (in acres/hectares)
   • Water availability (low, medium, high)
   • Weather conditions (season, rainfall)
   • Current market prices
3. Click Get Recommendation.
4. View results:
   • ✅ Suggested crop
   • ✅ Estimated ROI

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🔧 Configuration

• Weather Data: Connect to APIs (e.g., IMD, OpenWeather) for real-time forecasts.
• Market Prices: Integrate with government databases or update manually.
• Model Updates: Retrain periodically with fresh agricultural datasets for improved accuracy.

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📊 Example Workflow

Inputs:

• Land: 5 acres
• Water: Medium availability
• Season: Monsoon
• Prices: Paddy ₹18/kg, Maize ₹14/kg, Pulses ₹55/kg

Output:

• Recommended Crop: Paddy 🌾
• Estimated ROI: ₹60,000 per season

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🚀 Future Enhancements

• 🔄 Multi-crop Planning – Suggest crop rotation for better soil health.
• 📡 IoT Sensor Integration – Use real-time soil and weather sensors.
• ☁️ Cloud Deployment – Scalable solution for nationwide adoption.
• 📈 Market Forecasting – Predict future crop demand and prices.

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🛠 Troubleshooting

• ❌ Flask server not starting?

  • Check Python version (≥3.8)
  • Ensure Flask is installed

• ❌ Model not loading?

  • Confirm model files exist in /models folder

• ❌ Frontend not displaying results?

  • Check browser console for API errors
  • Verify backend is running

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👥 Contributors

This project was developed by Team Obsidian:

• aakashmutum
• midnyghtsky
• lavanyagupta1711
• NIkhith-Pelluri

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📜 License

This project currently does not include a license.
By default, all rights are reserved by the authors.