AgML aspires to identify key research gaps and opportunities at the intersection of agricultural modelling and machine learning research and support enhanced collaboration and engagement between experts in these disciplines.

Machine Learning based Crop Yield Prediction

🌾 A Reproducible Pipeline for Processing the Global Dataset of Historical Yields (1981–2016) by Iizumi et al.

This data package provides a spatially and temporally broad, multivariate dataset that quantifies trade-offs and co-benefits of tillage management in North American row-crop systems. Treatment-level means are provided for multiple ecosystem services, including crop yield, soil organic carbon (SOC) stocks, and N2O emissions, with detailed metadata.

Crop yield prediction using ML on real-world weather, pesticide, and agricultural data.

Flask web app that predicts crop yield using CatBoost, XGBoost, and LSTM models.

Plan Disease Prediction Using Machine Learning Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

XAI for soybean crop yield prediction using ConvLSTM

Crop yield regression from NDVI and multispectral satellite bands

Sustainable Crop Yield Prediction using Machine Learning

agroInsight Kenya - machine learning crop yield prediction for Kenyan farmers -Streamlit dashboard

Description: 🌱 Smart Farm Yield Prediction Dashboard using PySpark, Streamlit, and Machine Learning for crop yield forecasting and interactive visual insights.

Using Docker and Keras to generate competition-worthy Crop Yield Prediction Models | ISTA429 Midterm project for Team X for MCLAS2021

Published in IJITRA - Accessible Crop Yield Prediction Using Decision Trees: A Farmer-Oriented Web-Based AI Application