Skip to content

This repo contains the Minor Project 1 named Fasal Fusion: An Algorithmic Approach to Transform Crop Recommendations

Notifications You must be signed in to change notification settings

Manav-Khandurie/FASAL-FUSION

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

79 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Group 26

About the Project

The difficulty in modern agriculture is to optimize crop selection and resource management in the face of variable soil conditions. Traditional approaches are inefficient and have little technological leverage. This project aims to solve the problem by combining advanced algorithms, and a recommendation system. The goal is to provide farmers with a comprehensive decision-making framework that promotes data-driven decisions, increased crop productivity, and sustainable farming practices. The goal of this project is to bridge the gap between traditional agricultural wisdom and modern technology.

Tech Stack

Static Badge Static Badge Static Badge Static Badge

Architecture

fasalfusion2

Contributing

  1. Fork the Project
  2. Clone your forked repository
 git clone https://github.com/<your_github_username>/FASAL-FUSION.git
  1. Now go ahead and create a new branch and move to the branch

    git checkout -b fix-issue-<ISSUE-NUMBER>
  2. After you have added your changes, follow the following command chain

    • Check the changed files
     git status -s
    • Add all the files to the staging area
      git add .
      or
      git add <file_name1> <file_name2>
    • Commit your changes
     git commit -m "<EXPLAIN-YOUR_CHANGES>"
  3. Push your changes

    git push origin fix-issue-<ISSUE-NUMBER>
  4. Open a Pull Request

  • Wait for the PR to be reviewed and merged.

  • Happy Coding!


Repobeats analytics image

About

This repo contains the Minor Project 1 named Fasal Fusion: An Algorithmic Approach to Transform Crop Recommendations

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 93.3%
  • JavaScript 2.7%
  • SCSS 1.2%
  • Java 1.1%
  • HTML 0.9%
  • CSS 0.7%
  • HCL 0.1%