Agri-Hub/AAAI23-Eval-AgriRecommendations
"Evaluating Digital Agriculture Recommendations with Causal Inference". It was accepted and presented in the special track on Artificial Intelligence for Social Impact, AAAI-23
This project helps agricultural cooperatives and farm managers assess the real-world impact of digital farming recommendations, such as optimal sowing dates, on crop yield. By analyzing farm records, satellite data, and weather information, it determines if recommended practices actually lead to better harvests. This is for agronomists, cooperative managers, and agricultural policymakers who need to justify or improve their digital agriculture tools.
No commits in the last 6 months.
Use this if you need to rigorously evaluate whether specific agricultural recommendations or digital tools are genuinely improving farm performance indicators like crop yield.
Not ideal if you're looking for a system to generate new recommendations, or if you don't have historical farm data combined with environmental observations.
Stars
8
Forks
3
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 28, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Agri-Hub/AAAI23-Eval-AgriRecommendations"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
omroy07/AgriTech
AgriTech is an AI-powered web platform that offers crop recommendations, yield prediction,...
Project-AgML/AgML
AgML is a centralized framework for agricultural machine learning. AgML provides access to...
Gladiator07/Harvestify
A machine learning based website that recommends the best crop to grow, fertilizers to use, and...
vannu07/Farm-IQ-AI-Powered-Smart-Farming-Assistant
An intelligent system leveraging Machine Learning (ML) models to analyze soil health, weather,...
twitter-research/image-crop-analysis
Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness...