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A web application that uses AI to recommend the best crops for farmers based on soil and climate data. It includes blockchain integration for secure seed purchases, offering farmers data-driven insights to improve productivity and sustainability.

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Baisakhi AI

Empowering Agriculture with AI


🔗 Project Live at: Replit

📊 Datasets from:

🎥 Demo Video: Loom


👥 Team Ctrl Z

  • Divyansh Gaba (CSAM, 2nd Year, IIIT-D)
  • Tanisha Bansal (CSAI, 1st Year, IGDTUW)
  • Arnav Gupta (CSAM, 2nd Year, IIIT-D)

🛠 Problem Statement

Crop Guidance and Farmer's Friend - (BioTech)

Agriculture today faces numerous challenges that traditional methods can no longer address effectively. Farmers struggle with adapting to shifting customer demands while meeting the need for high-yield, high-quality, and efficient production. To modernize agriculture and bridge this gap, it is imperative to leverage the potential of AI to analyze historical data and provide data-driven solutions that empower farmers.


💡 Our Solution

Baisakhi AI aims to revolutionize the agricultural landscape by providing a comprehensive AI-powered platform that assists farmers in optimizing their crop selection and improving agricultural practices. The solution is built around the following key principles:

  1. Data-Driven Decisions: The platform leverages AI models trained on over 20 years of agricultural data to recommend the best crops for a farmer’s land.
  2. Soil Analysis: By analyzing soil parameters such as pH, nutrient levels, and climate conditions, the platform generates tailored recommendations.
  3. Web3 Integration: Secure blockchain-based transactions allow farmers to purchase seeds for recommended crops using Ethereum, ensuring transparency and trust.

This solution addresses the twin goals of enhancing profitability and promoting sustainability for farmers, equipping them with tools to navigate the challenges of modern agriculture.


⚙️ Implementation

The project was developed using the Django framework and the scikit-learn library. The implementation includes the following steps:

  1. Input Form Creation: A user-friendly web form was developed using Django, allowing farmers to input soil parameters like pH, temperature, and nutrient levels.
  2. Model Training: Machine learning models were trained on extensive datasets from FAO and Kaggle to predict suitable crops based on soil and climate conditions.
  3. Recommendation System: The trained model analyzes the input data to generate a ranked list of suitable crops, providing insights into water requirements, temperature tolerance, and potential yield.
  4. Result Display: Farmers receive a detailed report with crop recommendations, including expected yields based on their land size.
  5. Security and Authentication: Robust security measures ensure that the platform remains secure and accessible only to authorized users.
  6. Deployment: The platform is deployed on a reliable web server, making it easily accessible to farmers worldwide.

🚀 Key Features

  1. Soil Parameter Input: Intuitive forms for entering soil and environmental data, stored securely in a database.
  2. AI-Powered Recommendations: Machine learning models analyze data to provide accurate crop recommendations tailored to individual needs.
  3. Blockchain Integration: Secure and transparent transactions for seed purchases using Ethereum.
  4. Scalable and Customizable: The platform is designed to scale efficiently and can be tailored for specific regions or farming communities.
  5. User-Friendly Interface: The application features a clean and intuitive interface, ensuring ease of use for all users.
  6. Security and Authentication: In-built mechanisms to protect user data and ensure secure access.
  7. Comprehensive Insights: Detailed reports include crop suitability metrics and yield projections, aiding informed decision-making.

🛠 Tech Stack

  • Programming Languages: Python
  • Frameworks: Django
  • Libraries: Scikit-learn, Pandas, Numpy
  • Frontend: HTML, CSS, Bootstrap, JavaScript
  • Blockchain: Ethereum, Polygon
  • Tools: Python Virtual Environment

📦 Installation Guide

Follow these steps to set up and run the project locally:

# Clone the repository
$ git clone https://github.com/arnavgupta2003/BaisakhiAI.git

# Navigate to the project directory
$ cd BaisakhiAI

# Create and activate a Python virtual environment
$ virtualenv myEnv
$ source myEnv/bin/activate  # For Windows: myEnv\Scripts\activate

# Install required dependencies
$ pip install -r requirements.txt

# Apply database migrations
$ python manage.py migrate

# Start the development server
$ python manage.py runserver

Access the application at: http://127.0.0.1:8000


📸 Visual Overview

Dashboard

Screenshot 1

Soil Parameter Input Form

Screenshot 2 Screenshot 2

Crop Recommendations and Secure Transactions

Screenshot 3


✨ Upcoming Features

  1. Data Visualization: Enhanced visual representations of soil parameters and crop recommendations to make insights more accessible.
  2. Admin Interface: A dedicated interface for administrators to manage and update data easily.
  3. Expert Network: A platform connecting farmers with agricultural experts and global farming communities for additional guidance.
  4. Animal Husbandry Module: AI-powered solutions for managing challenges in animal farming, including breed selection and environment optimization.
  5. Delivery Tracking: A robust system to ensure transparency and prevent fraud in seed and supply delivery.

🌟 Conclusion

Baisakhi AI is a groundbreaking tool designed to assist farmers in making data-driven decisions to enhance productivity and sustainability. By offering tailored crop recommendations based on comprehensive soil analysis, the platform addresses the pressing challenges of modern agriculture.

Key benefits include:

  • Increased profitability through optimized crop selection.
  • Reduced costs and labor by leveraging data-driven insights.
  • Enhanced sustainability through informed resource usage.

With its user-friendly interface, secure blockchain integration, and scalability, Baisakhi AI is poised to revolutionize farming practices globally, contributing to the sustainable development of the agricultural sector.


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A web application that uses AI to recommend the best crops for farmers based on soil and climate data. It includes blockchain integration for secure seed purchases, offering farmers data-driven insights to improve productivity and sustainability.

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