https://github.com/dhhruv/kisaani
"Kisaani" is an application that takes required parameters intelligently or from the database of the location (from the cloud) and provides the list of best crops suited for that land. The application should also be able to collect the outcome after cultivation and apply correction as appropriate for further advisories. The details of the crops for the region and conditions are provided. Applications should be interactive, user friendly for farmers (provide local language support) and should provide support in real time.
https://github.com/dhhruv/kisaani
crop crop-recommendation data-science ieee ieee-hackathon machine-learning
Last synced: 2 months ago
JSON representation
"Kisaani" is an application that takes required parameters intelligently or from the database of the location (from the cloud) and provides the list of best crops suited for that land. The application should also be able to collect the outcome after cultivation and apply correction as appropriate for further advisories. The details of the crops for the region and conditions are provided. Applications should be interactive, user friendly for farmers (provide local language support) and should provide support in real time.
- Host: GitHub
- URL: https://github.com/dhhruv/kisaani
- Owner: dhhruv
- Created: 2020-10-26T13:01:21.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-04-29T05:33:09.000Z (about 4 years ago)
- Last Synced: 2025-01-12T07:08:47.400Z (4 months ago)
- Topics: crop, crop-recommendation, data-science, ieee, ieee-hackathon, machine-learning
- Homepage:
- Size: 15.8 MB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Kisaani
## Abstract:
- "Kisaani" is out extreme objective which will be a web application advancing natural and green cultivating/farming. It's ultimate goal is to provide aid to new farmers that are looking to grow organic crops, or simply just to learn what may be possible to grow effectively in their area. We are a team of computer scientists and machine learning enthusiasts interested in producing an intelligent model that can learn, over time, particular weather and environmental features tied to certain array of crops or produce. Our model will be trained in future with over six thousand weather stations throughout the nation and supplied with a list of currently grown crops at certified organic farms. The end user can simply enter their zipcode and then the application uses this model in order to intelligently produce crop recommendations that best matches the farmer's environment and circumstances.
## Motivation:
- Our motivation is to build tools that use machine learning methods and strategies in order to support the current ecological movement of growing foods with least synthetic imtervention. Thus, this project in the Hackathon grew out of a need for an online resource that gathers information about "green" farms around the nation and provide these resources in a aggregated way to users.