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.

No description provided.

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.

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.

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

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

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