🌾 CropAI - AI-Powered Crop Recommendation System

Revolutionizing Agriculture with AI-Powered Crop Recommendations

🚀 Live Demo | 📖 Documentation | 🔧 Installation

🌟 Overview

CropAI is an intelligent agricultural platform that leverages artificial intelligence to provide personalized crop recommendations for farmers. By analyzing comprehensive farming data including soil health, climate conditions, historical yields, and government MSP (Minimum Support Price) data, CropAI helps farmers make data-driven decisions to maximize profitability and sustainability.

✨ Key Features

🤖 AI-Powered Recommendations: Uses advanced AI models (OpenAI GPT-4 or Together AI Mistral-7B)
📊 MSP Integration: Real-time integration with Government of India MSP data (2015-2026)
🌱 Comprehensive Analysis: Considers soil health, climate, land details, and farmer profiles
💰 Profit Optimization: Calculates expected profits, yields, and resource requirements
🔄 Alternative Options: Provides multiple crop alternatives with detailed comparisons
📱 Modern UI: Responsive React interface with intuitive farmer data collection
🛡️ Risk Assessment: Evaluates crop risks and provides mitigation strategies

🏗️ Architecture

Project Structure

CropAI/
├── backend/                      # Node.js/Express API Server
│   ├── server.js                # Main Express server and entry point
│   ├── routers/                 # API route handlers
│   │   ├── recommendation.js    # Crop recommendation endpoint
│   │   ├── lossAnalysis.js      # Crop loss analysis endpoint
│   │   ├── naturalFarming.js    # Natural farming recommendations
│   │   ├── profitableCrops.js   # Profitable crops analysis
│   │   └── nextCropInsights.js  # Next crop planning insights
│   ├── mspData.js               # Government MSP (Minimum Support Price) data
│   ├── apmcData.js              # APMC (Agricultural Produce Market Committee) data
│   ├── profitabilityEngine.js   # Crop profitability calculation engine
│   ├── llmService.js            # LLM (AI) service integration
│   ├── sample-request.json      # Sample API request data
│   ├── package.json             # Backend dependencies
│   ├── .env                     # Environment variables (create this)
│   └── node_modules/            # Backend dependencies (generated)
│
├── frontend/                     # React Application (Vite)
│   ├── src/
│   │   ├── App.jsx              # Main application component
│   │   ├── main.jsx             # React application entry point
│   │   ├── App.css              # Application styles
│   │   ├── index.css            # Global styles and Tailwind imports
│   │   ├── i18n.ts              # Internationalization configuration
│   │   └── assets/              # Static assets (images, icons)
│   ├── index.html               # HTML template
│   ├── package.json             # Frontend dependencies
│   ├── vite.config.ts           # Vite build configuration
│   ├── tailwind.config.js       # TailwindCSS configuration
│   ├── tsconfig.json            # TypeScript configuration
│   ├── eslint.config.js         # ESLint configuration
│   └── node_modules/            # Frontend dependencies (generated)
│
├── API_DOCUMENTATION.md          # Detailed API documentation
└── README.md                     # This file - Project documentation

Key Files Explained

Backend:

server.js: Express server setup, middleware configuration, route mounting
routers/*.js: Individual route handlers for each API endpoint
mspData.js: Government MSP data for crop pricing (2015-2026)
apmcData.js: APMC market data for crop demand and pricing
profitabilityEngine.js: Core logic for calculating crop profitability
llmService.js: Integration with OpenAI/Together AI for AI-powered insights

Frontend:

App.jsx: Main React component with form handling and API integration
main.jsx: React DOM rendering and app initialization
i18n.ts: Internationalization setup (supports multiple languages)
index.css: Global styles and TailwindCSS imports

Technology Stack

Backend:

Runtime: Node.js 18+
Framework: Express.js 5.1+
AI Services: OpenAI SDK, Together AI SDK
Environment: dotenv for configuration

Frontend:

Framework: React 19+
Build Tool: Vite 7+
Styling: TailwindCSS 3.4+
i18n: react-i18next
Language: TypeScript (config) + JavaScript (implementation)

🚀 Quick Start

Prerequisites

Before you begin, ensure you have the following installed on your system:

Required Software

Node.js (Version 18.0.0 or higher)

Download from: nodejs.org

Verify installation:

node --version  # Should show v18.x.x or higher
npm --version   # Should show 9.x.x or higher

npm (comes with Node.js)
- Verify installation:
```
npm --version
```
Git (for cloning the repository)
- Download from: git-scm.com
- Verify installation:
```
git --version
```

API Keys (Required for AI Features)

You'll need at least one of the following API keys:

Together AI (Recommended - Free tier available)
- Sign up at: together.ai
- Get your API key from the dashboard
- Free tier includes generous credits for testing
OpenAI (Alternative)
- Sign up at: platform.openai.com
- Get your API key from API Keys section
- Requires paid credits

Note: The application will work without API keys, but AI-powered features will be disabled. You can still test the API structure and data processing.

Installation

Follow these steps to set up the project locally:

Step 1: Clone the Repository

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

Verify: You should see backend/ and frontend/ directories.

Step 2: Backend Setup

Navigate to backend directory
```
cd backend
```
Install dependencies
```
npm install
```
Expected output: Dependencies will be installed. This may take 1-2 minutes.

Verify installation:
```
ls node_modules  # Should show installed packages
```

Create environment file

Create a .env file in the backend/ directory:

touch .env

Or on Windows:

type nul > .env

Add the following content to .env:

# Server Configuration
PORT=5000
NODE_ENV=development

# AI Service Configuration (choose at least one)
# Together AI (Recommended - Free tier available)
TOGETHER_API_KEY=your_together_api_key_here

# OpenAI (Alternative)
# OPENAI_API_KEY=your_openai_api_key_here

# Database (Optional - for future use)
# MONGODB_URI=mongodb://localhost:27017/cropai

Important: Replace your_together_api_key_here with your actual API key.

Start the backend server
```
npm run dev
```
Expected output:
```
Server running on port 5000
```
Verify backend is running:
- Open http://localhost:5000 in your browser
- You should see: {"message":"Crop Recommendation API"}
- Or test with curl:
```
curl http://localhost:5000
```

Step 3: Frontend Setup (New Terminal)

Open a new terminal window (keep backend running)

Navigate to frontend directory

cd frontend
# If you're in the project root:
cd /path/to/CropAI/frontend

Install dependencies
```
npm install
```
Expected output: Dependencies will be installed. This may take 2-3 minutes.

Verify installation:
```
ls node_modules  # Should show installed packages
```

Start the frontend development server

npm run dev

Expected output:

VITE v7.x.x  ready in xxx ms

➜  Local:   http://localhost:5173/
➜  Network: use --host to expose

Step 4: Access the Application

Once both servers are running:

Frontend Application: http://localhost:5173
- Open in your browser
- You should see the CropAI interface
Backend API: http://localhost:5000
- API base URL: http://localhost:5000/api
- Health check: http://localhost:5000

Step 5: Verify Everything Works

Test Backend API:
```
curl http://localhost:5000
```
Should return: {"message":"Crop Recommendation API"}
Test Frontend:
- Open http://localhost:5173
- You should see the CropAI homepage
- Try loading sample data using the sample data buttons

Test API Endpoint (with sample data):

curl -X POST http://localhost:5000/api/recommendations \
  -H "Content-Type: application/json" \
  -d '{
    "soilType": "loam",
    "phLevel": 7.2,
    "rainfall": 1200,
    "temperature": 28,
    "currentCrop": "Rice",
    "yieldHistory": 3.5,
    "marketPrice": 25
  }'

Note: If you haven't set up API keys, you may see an error about AI service configuration, but the API structure should still respond.

Running Both Services Simultaneously

You need two terminal windows:

Terminal 1: Backend server (cd backend && npm run dev)
Terminal 2: Frontend server (cd frontend && npm run dev)

Tip: Use a terminal multiplexer like tmux or screen, or use VS Code's integrated terminal with split panes.

🔧 Configuration

Environment Variables

The backend requires a .env file in the backend/ directory. Create it if it doesn't exist:

cd backend
touch .env  # or type nul > .env on Windows

Complete `.env` File Template

# ============================================
# Server Configuration
# ============================================
PORT=5000
NODE_ENV=development

# ============================================
# AI Service Configuration
# ============================================
# You need at least ONE of the following API keys
# Together AI is recommended (free tier available)

# Together AI (Recommended)
# Get your API key from: https://together.ai/
TOGETHER_API_KEY=your_together_api_key_here

# OpenAI (Alternative)
# Get your API key from: https://platform.openai.com/api-keys
# OPENAI_API_KEY=your_openai_api_key_here

# ============================================
# Database Configuration (Optional)
# ============================================
# MongoDB is not currently required, but reserved for future use
# MONGODB_URI=mongodb://localhost:27017/cropai

Environment Variable Details

Variable	Required	Description	Default
`PORT`	No	Backend server port	`5000`
`NODE_ENV`	No	Environment mode (`development` or `production`)	`development`
`TOGETHER_API_KEY`	Yes*	Together AI API key for LLM features	-
`OPENAI_API_KEY`	Yes*	OpenAI API key (alternative to Together AI)	-
`MONGODB_URI`	No	MongoDB connection string (future use)	-

* At least one AI API key is required for AI-powered features to work.

Getting API Keys

Together AI (Recommended):

Visit together.ai
Sign up for a free account
Navigate to API Keys section
Create a new API key
Copy the key to your .env file

OpenAI:

Visit platform.openai.com
Sign up or log in
Go to API Keys section
Create a new secret key
Copy the key to your .env file
Note: Requires paid credits

AI Provider Selection

CropAI supports two AI providers:

Together AI (Recommended)
- Model: Meta-Llama-3.1-8B-Instruct-Turbo
- Free tier available with generous credits
- Cost-effective for production
- Good performance for agricultural recommendations
OpenAI
- Model: GPT-4o-mini
- Higher accuracy and reasoning
- Requires paid credits
- Better for complex analysis

How to Configure:

Set the API key in .env file
The backend automatically uses Together AI if TOGETHER_API_KEY is set
Falls back to OpenAI if only OPENAI_API_KEY is available
You can also specify aiProvider: "together" or aiProvider: "openai" in API requests

Frontend Configuration (Optional)

The frontend can be configured via environment variables. Create .env.local in the frontend/ directory:

# API Base URL
VITE_API_BASE_URL=http://localhost:5000/api

# Feature Flags
VITE_ENABLE_DEBUG=true
VITE_DEFAULT_AI_PROVIDER=together

Note: Frontend environment variables must be prefixed with VITE_ to be accessible in the React app.

📖 API Documentation

Base URL

http://localhost:5000/api

Authentication

Currently, no authentication required. Add API keys for production use.

Content Type

All requests must use Content-Type: application/json header.

CORS Configuration

The backend is configured to accept requests from http://localhost:5173 (frontend). For other origins, update CORS settings in backend/server.js.

Endpoints Overview

The API provides 5 main endpoint groups:

/api/recommendations - AI-powered crop recommendations
/api/loss-analysis - Analyze crop losses and provide recommendations
/api/natural-farming - Natural farming practice recommendations
/api/profitable-crops - Get top profitable crops based on market data
/api/next-crop-insights - Comprehensive next crop planning insights

1. POST `/api/recommendations`

Get AI-powered crop recommendations based on comprehensive farmer data.

Endpoint: POST /api/recommendations

Request Body:

{
  "soilType": "loam",
  "phLevel": 7.2,
  "rainfall": 1200,
  "temperature": 28,
  "currentCrop": "Rice",
  "yieldHistory": 3.5,
  "marketPrice": 25,
  "aiProvider": "together",
  "language": "en",
  "landDetails": {
    "state": "Andhra Pradesh",
    "district": "Chittoor",
    "subDistrict": "Kuppam",
    "village": "Kuppam Village",
    "surveyNumber": "789/B",
    "totalArea": 3.2,
    "extentAssignedArea": 2.8,
    "landSource": "Own Land"
  },
  "ownershipDetails": [{
    "ownerNumber": "001",
    "mainOwnerNumber": "001",
    "identifierName": "Farmer Name",
    "ownerType": "Individual",
    "ownerShareType": "Full Owner",
    "extent": 2.8
  }],
  "soilDetails": {
    "centreName": "Agricultural Research Center",
    "testId": "TEST-001",
    "testingDate": "2024-03-15",
    "validity": "2025-03-15",
    "surveyNo": "123/A",
    "plotSize": 2.3,
    "soilHealthParameters": {
      "nitrogen": 280,
      "phosphorus": 25,
      "potassium": 180,
      "ph": 7.2,
      "organicCarbon": 0.8,
      "sulphur": 15,
      "zinc": 1.2
    }
  },
  "cropDetailsEPanta": {
    "farmerName": "Farmer Name",
    "aadhaarNumber": "123456789012",
    "mobileNumber": "9876543210",
    "variety": "MTU 1010",
    "areaSown": 2.3,
    "dateOfSowing": "2024-07-15",
    "cropNature": "Kharif",
    "waterSource": "Canal",
    "methodOfIrrigation": "Drip Irrigation",
    "farmingType": "Organic"
  },
  "cropDetailsFarmer": {
    "investmentKharif": "₹85000",
    "cropYield": "4.2 tons/ha",
    "farmerAssets": ["Tractor", "Irrigation Pump"],
    "stages": {
      "preSowing": "Fertilizer application",
      "sowing": "Manual sowing",
      "germination": "70% germination",
      "vegetative": "Good growth",
      "flowering": "Dense flowering",
      "fruitingMaturity": "Grain filling",
      "harvesting": "Manual harvesting",
      "postHarvest": "Threshing and storage"
    }
  },
  "cropInsurance": {
    "insured": true,
    "sumInsured": "₹50000",
    "premiumPaid": "₹750",
    "coverageArea": 2.3,
    "riskCoverage": "Flood, Drought, Pest Attack"
  },
  "encumbrance": {
    "status": "Clear",
    "mortgageDetails": "Nil",
    "encumbranceIfAny": "No encumbrance"
  }
}

Response:

{
  "success": true,
  "recommendation": {
    "recommendedCrop": "Tur (Arhar)",
    "reason": "High MSP rates and suitable for loam soil conditions",
    "confidenceScore": 85,
    "expectedProfit": 80000,
    "expectedYield": 2.0,
    "waterRequirement": 2000,
    "laborRequirement": 100,
    "maturityTime": 110,
    "riskLevel": "Medium",
    "equipmentNeeded": ["Tractor", "Plough"],
    "effortDistribution": {
      "setup": 20,
      "maintenance": 50,
      "harvesting": 30
    },
    "resourceRequirements": {
      "waterNeeded": "2000 L/ha",
      "fertilizers": ["Urea", "DAP"],
      "pesticides": ["Generic Pesticide"]
    },
    "soilConditions": {
      "suitability": "Good",
      "analysis": "Tur is suitable for loam soil with pH 7.2"
    },
    "marketAnalysis": {
      "profitMargin": "25%",
      "marketDemand": "Medium"
    },
    "alternativeCrops": [
      {
        "crop": "Groundnut",
        "score": 75,
        "reason": "Alternative oilseed crop with stable market demand",
        "expectedProfit": 70000,
        "expectedYield": 1.8,
        "waterRequirement": 1800,
        "laborRequirement": 90,
        "maturityTime": 105,
        "riskLevel": "Medium",
        "equipmentNeeded": ["Tractor"],
        "resourceRequirements": {
          "waterNeeded": "1800 L/ha",
          "fertilizers": ["DAP"],
          "pesticides": ["Cypermethrin"]
        }
      }
    ]
  }
}

Error Response:

{
  "error": "All crop data fields are required",
  "details": "Missing required field: soilType"
}

2. POST `/api/loss-analysis/analyze`

Analyze crop losses and provide detailed recommendations for improvement.

Endpoint: POST /api/loss-analysis/analyze

Required Fields:

actualYield (number) - Actual yield in quintals
costs (object) - Cost breakdown with fields like labour, seeds, fertilizers, pesticides, irrigation, equipment, transportation, other
income (object) - Income breakdown with fields like yieldSale, byproducts, residue, subsidy

Optional Fields:

cropName (string) - Name of the crop
season (string) - Season (Kharif/Rabi/Zaid) - auto-inferred if not provided
landArea (number) - Land area in hectares
location (object) - Location details with village, mandal
expectedYield (number) - Expected yield in quintals
cropDetails (object) - Additional crop details
challenges (array) - Array of challenges faced

Example Request:

{
  "cropName": "Rice",
  "season": "Kharif 2024",
  "landArea": 2.5,
  "location": {
    "village": "Kuppam",
    "mandal": "Kuppam Mandal"
  },
  "actualYield": 45,
  "expectedYield": 60,
  "costs": {
    "labour": 25000,
    "seeds": 5000,
    "fertilizers": 15000,
    "pesticides": 8000,
    "irrigation": 12000,
    "equipment": 5000,
    "transportation": 3000,
    "other": 2000
  },
  "income": {
    "yieldSale": 90000,
    "byproducts": 5000,
    "residue": 3000,
    "subsidy": 2000
  },
  "cropDetails": {
    "waterSource": "Bore Well",
    "irrigationMethod": "Drip Irrigation",
    "sowingDate": "15-07-2024",
    "harvestDate": "15-11-2024",
    "soilType": "Red Soil"
  },
  "challenges": [
    "Pest attack in flowering stage",
    "Water scarcity in August",
    "High fertilizer costs"
  ]
}

Example Response:

{
  "success": true,
  "data": {
    "cropName": "Rice",
    "season": "Kharif 2024",
    "summary": {
      "totalCost": 73000,
      "totalIncome": 100000,
      "netProfit": 27000,
      "profitMargin": "27.00",
      "actualYield": 45,
      "expectedYield": 60,
      "yieldGap": 15
    },
    "analysis": "Comprehensive AI-generated analysis with primary factors and recommendations...",
    "timestamp": "2024-12-01T10:30:00.000Z"
  }
}

3. POST `/api/natural-farming/recommendations`

Get natural farming practice recommendations for transitioning to sustainable agriculture.

Endpoint: POST /api/natural-farming/recommendations

Required Fields:

location (object) - Must include mandal field
- village (string, optional)
- mandal (string, required)

Optional Fields:

landArea (number) - Land area in hectares (default: 1)
currentCrop (string) - Current crop being grown (default: "mixed crops")
soilType (string) - Soil type (default: "red soil")
waterSource (string) - Water source (default: "bore well")
currentPractices (object) - Current farming practices
- usesChemicalFertilizers (boolean)
- usesPesticides (boolean)
- irrigationMethod (string)
- hasLivestock (boolean)
challenges (array) - Array of current challenges

Example Request:

{
  "location": {
    "village": "Kuppam",
    "mandal": "Kuppam Mandal"
  },
  "landArea": 2.5,
  "currentCrop": "Groundnut",
  "soilType": "Red Soil",
  "waterSource": "Bore Well",
  "currentPractices": {
    "usesChemicalFertilizers": true,
    "usesPesticides": true,
    "irrigationMethod": "Drip Irrigation",
    "hasLivestock": true
  },
  "challenges": [
    "High input costs",
    "Soil degradation",
    "Water scarcity"
  ]
}

Example Response:

{
  "success": true,
  "data": {
    "location": {
      "village": "Kuppam",
      "mandal": "Kuppam Mandal",
      "region": "Andhra Pradesh"
    },
    "landArea": 2.5,
    "currentCrop": "Groundnut",
    "benefits": [
      "Reduce input costs by 40-60%",
      "Improve soil organic matter from 0.5% to 2% in 2-3 years",
      "Enhance water retention capacity by 30-40%"
    ],
    "recommendedPractices": [
      {
        "category": "Soil Health & Fertility Management",
        "name": "Jeevamrutham Application",
        "description": "Detailed steps for preparing and applying Jeevamrutham..."
      }
    ],
    "rawAnalysis": "Full AI-generated analysis...",
    "timestamp": "2024-12-01T10:30:00.000Z"
  }
}

4. POST `/api/profitable-crops`

Get top profitable crops based on market data and farmer profile.

Endpoint: POST /api/profitable-crops

Optional Fields:

farmerData (object) - Farmer profile data (optional)
region (string) - Region name (default: "KUPPAM/PALAMANER")
topN (number) - Number of crops to return (default: 5)

Example Request:

{
  "farmerData": {
    "profile": {
      "metaData": {
        "masterData": {
          "agriStack": {
            "totalAreaHectares": 2.5
          }
        }
      }
    }
  },
  "region": "KUPPAM",
  "topN": 5
}

Example Response:

{
  "success": true,
  "region": "KUPPAM",
  "totalCropsAnalyzed": 25,
  "recommendations": [
    {
      "crop": "TOMATO",
      "expectedIncome": "₹1,25,000",
      "demand": "High",
      "successRate": "85%",
      "details": {
        "operationalDetails": "Detailed operational guidance...",
        "season": "Kharif/Rabi",
        "duration": "90-120 days"
      },
      "marketInfo": {
        "recentTrades": 150,
        "totalArrivals": 5000,
        "avgPrice": "₹2,500/quintal",
        "volatility": "Medium"
      }
    }
  ]
}

Additional Profitable Crops Endpoints

GET /api/profitable-crops/high-demand

Returns crops with high market demand
No request body required

GET /api/profitable-crops/market-overview

Returns market overview for all crops
No request body required

POST /api/profitable-crops/analyze

Detailed analysis for a specific crop
Requires: cropName (string)
Optional: farmerData (object)

5. POST `/api/next-crop-insights`

Get comprehensive insights for planning the next crop cycle, including crop recommendations, government schemes, and operational guidance.

Endpoint: POST /api/next-crop-insights

Request Body: Accepts comprehensive farmer profile data similar to /api/recommendations, plus additional fields for crop planning.

Example Request:

{
  "farmerData": {
    "profile": {
      "address": "Kuppam",
      "metaData": {
        "masterData": {
          "agriStack": {
            "totalAreaHectares": 2.5,
            "soilType": "Red Soil",
            "waterSource": "Bore Well"
          }
        }
      }
    }
  },
  "currentCrop": "Rice",
  "season": "Kharif",
  "region": "KUPPAM"
}

Example Response:

{
  "success": true,
  "insights": {
    "recommendedCrops": [...],
    "governmentSchemes": [...],
    "operationalGuidance": {...},
    "marketAnalysis": {...},
    "riskAssessment": {...}
  }
}

Testing API Endpoints

Using cURL

Test Recommendations Endpoint:

curl -X POST http://localhost:5000/api/recommendations \
  -H "Content-Type: application/json" \
  -d '{
    "soilType": "loam",
    "phLevel": 7.2,
    "rainfall": 1200,
    "temperature": 28,
    "currentCrop": "Rice",
    "yieldHistory": 3.5,
    "marketPrice": 25
  }'

Test Loss Analysis:

curl -X POST http://localhost:5000/api/loss-analysis/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "actualYield": 45,
    "expectedYield": 60,
    "costs": {"labour": 25000, "seeds": 5000},
    "income": {"yieldSale": 90000}
  }'

Test High Demand Crops:

curl http://localhost:5000/api/profitable-crops/high-demand

Using Postman

Import the collection (if available)
Set base URL: http://localhost:5000/api
Use POST method for endpoints requiring data
Set Content-Type: application/json header
Add request body in JSON format

Using Frontend

The React frontend at http://localhost:5173 provides a UI for testing all endpoints with sample data.

✅ Verification & Testing

After completing the installation, verify that everything is working correctly:

Step 1: Verify Backend Server

Check if backend is running:

curl http://localhost:5000

Expected Response:

{"message":"Crop Recommendation API"}

Test API endpoint:

curl -X POST http://localhost:5000/api/recommendations \
  -H "Content-Type: application/json" \
  -d '{
    "soilType": "loam",
    "phLevel": 7.2,
    "rainfall": 1200,
    "temperature": 28,
    "currentCrop": "Rice",
    "yieldHistory": 3.5,
    "marketPrice": 25
  }'

Expected Response: JSON with success: true and recommendation data

Note: If API keys are not configured, you may see an error about AI service, but the API structure should still respond.

Step 2: Verify Frontend Application

Open browser: Navigate to http://localhost:5173
Check for errors:
- Open browser DevTools (F12)
- Check Console tab for any errors
- Check Network tab to verify API calls
Test sample data:
- Click on any "Load Sample Data" button
- Verify form fields are populated
- Submit the form to test API integration

Step 3: Test All API Endpoints

Test Loss Analysis:

curl -X POST http://localhost:5000/api/loss-analysis/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "actualYield": 45,
    "expectedYield": 60,
    "costs": {"labour": 25000, "seeds": 5000},
    "income": {"yieldSale": 90000}
  }'

Test Natural Farming:

curl -X POST http://localhost:5000/api/natural-farming/recommendations \
  -H "Content-Type: application/json" \
  -d '{
    "location": {"mandal": "Kuppam Mandal"},
    "landArea": 2.5,
    "currentCrop": "Groundnut"
  }'

Test Profitable Crops:

curl http://localhost:5000/api/profitable-crops/high-demand

Step 4: Verify API Keys (If Configured)

If you've added API keys, test AI features:

Check backend logs for API key validation messages
Make a recommendation request and verify AI-generated content
Check for errors in console if API calls fail

Common Success Indicators

✅ Backend Working:

Server shows "Server running on port 5000"
curl http://localhost:5000 returns JSON response
No errors in backend terminal

✅ Frontend Working:

Page loads at http://localhost:5173
No console errors in browser DevTools
Forms are interactive and responsive

✅ API Integration Working:

Form submission triggers API call
Response data appears in UI
Network tab shows successful API requests

🔧 Troubleshooting

Common Issues and Solutions

Backend Issues

Problem: Port 5000 already in use

Error: listen EADDRINUSE: address already in use :::5000

Solution:

# Option 1: Kill the process using port 5000
# On macOS/Linux:
lsof -ti:5000 | xargs kill -9

# On Windows:
netstat -ano | findstr :5000
taskkill /PID <PID> /F

# Option 2: Change port in .env file
PORT=5001

Problem: Module not found errors

Error: Cannot find module 'express'

Solution:

cd backend
rm -rf node_modules package-lock.json
npm install

Problem: API key not working

Error: LLM service not configured

Solution:

Verify .env file exists in backend/ directory
Check API key is correctly set (no extra spaces)
Restart backend server after changing .env
Verify API key is valid by testing it directly with the provider

Problem: CORS errors

Access to fetch at 'http://localhost:5000/api/...' from origin 'http://localhost:5173' has been blocked by CORS policy

Solution:

CORS is already configured in backend/server.js

If you're using a different frontend URL, update CORS settings:

app.use(cors({
  origin: 'http://your-frontend-url:port'
}));

Frontend Issues

Problem: Port 5173 already in use

Error: Port 5173 is in use

Solution:

# Vite will automatically try the next available port
# Or specify a different port:
npm run dev -- --port 5174

Problem: Dependencies installation fails

npm ERR! code ERESOLVE

Solution:

cd frontend
rm -rf node_modules package-lock.json
npm install --legacy-peer-deps
# Or use:
npm install --force

Problem: Build errors

Error: Cannot find module 'react'

Solution:

cd frontend
rm -rf node_modules
npm install

Problem: API calls failing

Failed to fetch

Solution:

Verify backend is running on port 5000
Check browser console for detailed error
Verify CORS configuration
Check network tab in DevTools for request details

Environment Setup Issues

Problem: .env file not being read

process.env.TOGETHER_API_KEY is undefined

Solution:

Ensure .env file is in backend/ directory (not project root)
Verify file is named exactly .env (not .env.txt or .env.example)
Restart backend server after creating/modifying .env
Check for syntax errors in .env file (no spaces around =)

Problem: Node.js version mismatch

Error: The engine "node" is incompatible with this module

Solution:

# Check Node.js version
node --version  # Should be v18.0.0 or higher

# Update Node.js if needed:
# Using nvm (recommended):
nvm install 18
nvm use 18

# Or download from nodejs.org

API-Specific Issues

Problem: AI responses are slow or timeout

Cause: Large AI models can take 10-30 seconds
Solution: This is normal. Consider using Together AI for faster responses

Problem: Invalid API response format

Error: Failed to process AI response

Solution:

Check API key is valid and has credits
Verify network connection
Check backend logs for detailed error
Try a different AI provider

Problem: Missing required fields error

Error: All crop data fields are required

Solution:

Review API documentation for required fields
Use sample data from frontend as reference
Check request body format matches examples

Getting Help

If you encounter issues not covered here:

Check logs:
- Backend: Terminal where npm run dev is running
- Frontend: Browser DevTools Console
Verify setup:
- Node.js version: node --version (should be 18+)
- npm version: npm --version
- Dependencies installed: Check node_modules/ exists
Test components individually:
- Test backend API with curl/Postman
- Test frontend without backend (will show connection errors, but UI should load)
Common fixes:
- Delete node_modules and reinstall
- Clear browser cache
- Restart both servers
- Check .env file configuration
Still stuck?:
- Check GitHub Issues
- Review API Documentation
- Create a new issue with:
  - Error messages
  - Steps to reproduce
  - Node.js and npm versions
  - Operating system

🎯 Usage Examples

Basic Recommendation Request

const farmerData = {
  soilType: "loam",
  phLevel: 7.0,
  rainfall: 1000,
  temperature: 25,
  currentCrop: "Rice",
  yieldHistory: 3.0,
  marketPrice: 20,
  aiProvider: "together"
};

fetch('http://localhost:5000/api/recommendations', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify(farmerData),
})
.then(response => response.json())
.then(data => console.log(data.recommendation));

Using with React Frontend

The frontend provides a comprehensive form interface for data collection. Sample farmer data is pre-loaded for testing.

📊 Data Sources

MSP (Minimum Support Price) Data

Source: Government of India, Ministry of Agriculture
Coverage: 2015-2026 for major crops
Update Frequency: Annual (Kharif & Rabi seasons)
Crops Covered: Paddy, Wheat, Tur, Moong, Urad, Cotton, and more

AI Models

OpenAI GPT-4: Advanced reasoning and analysis
Together AI Mistral-7B: Efficient and cost-effective

🔒 Security & Privacy

Data Privacy: Farmer data is processed locally (no external storage)
API Security: Implement authentication for production use
Input Validation: Comprehensive validation of all input parameters
Error Handling: Secure error responses without data leakage

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

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

🙏 Acknowledgments

Government of India for MSP data and agricultural support
OpenAI and Together AI for AI model access
React & Node.js communities for excellent frameworks
KADA (Kuppam Area Development Authority) for domain expertise

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: support@cropai.com

Made with ❤️ for farmers worldwide

⬆️ Back to Top

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
backend		backend
frontend		frontend
.gitattributes		.gitattributes
.gitignore		.gitignore
API_DOCUMENTATION.md		API_DOCUMENTATION.md
README.md		README.md

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🌾 CropAI - AI-Powered Crop Recommendation System

🌟 Overview

✨ Key Features

🏗️ Architecture

Project Structure

Key Files Explained

Technology Stack

🚀 Quick Start

Prerequisites

Required Software

API Keys (Required for AI Features)

Installation

Step 1: Clone the Repository

Step 2: Backend Setup

Step 3: Frontend Setup (New Terminal)

Step 4: Access the Application

Step 5: Verify Everything Works

Running Both Services Simultaneously

🔧 Configuration

Environment Variables

Complete .env File Template

Environment Variable Details

Getting API Keys

AI Provider Selection

Frontend Configuration (Optional)

📖 API Documentation

Base URL

Authentication

Content Type

CORS Configuration

Endpoints Overview

1. POST /api/recommendations

2. POST /api/loss-analysis/analyze

3. POST /api/natural-farming/recommendations

4. POST /api/profitable-crops

Additional Profitable Crops Endpoints

5. POST /api/next-crop-insights

Testing API Endpoints

Using cURL

Using Postman

Using Frontend

✅ Verification & Testing

Step 1: Verify Backend Server

Step 2: Verify Frontend Application

Step 3: Test All API Endpoints

Step 4: Verify API Keys (If Configured)

Common Success Indicators

🔧 Troubleshooting

Common Issues and Solutions

Backend Issues

Frontend Issues

Environment Setup Issues

API-Specific Issues

Getting Help

🎯 Usage Examples

Basic Recommendation Request

Using with React Frontend

📊 Data Sources

MSP (Minimum Support Price) Data

AI Models

🔒 Security & Privacy

🤝 Contributing

📄 License

🙏 Acknowledgments

📞 Support

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Complete `.env` File Template

1. POST `/api/recommendations`

2. POST `/api/loss-analysis/analyze`

3. POST `/api/natural-farming/recommendations`

4. POST `/api/profitable-crops`

5. POST `/api/next-crop-insights`

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