:corn: Machine learning model for crop yield prediction

Futures are contracts to buy and sell commodities on a future date at a specified price. Corporations can use futures to hedge against price increases and ensure access to limited goods, but accurate prediction of future commodity values is essential for avoiding unwise purchases. Corn grain poses a special challenge because future prices reflect weather-dependent supply as well as variable demand. My role is to demonstrate how past publicly-available data can be used to predict future corn futures prices.

Corn Yield prediction as design project

Prediction of corn yield in multi-locations using multi-spectral satellite images (1st-place solution for "MLCAS Corn Yield Prediction Using Satellite Data" competition)

MLCAS Corn Yield Prediction Using Satellite Data

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The source code of paper "Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning"

Corn yield prediction in Montérégie (2010–2023) using XGBoost, Random Forest, SVM and TabResNet, Sentinel-1/2, Landsat, climate and soil data via Google Earth Engine.

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Machine learning system to predict household-level corn production in Kenya, supporting food security decisions and resource allocation.

solution for MLCAS 2024 Corn Yield Prediction hosted on EvalAI

Final Report for Stat 615

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The goal of the project is to develop a data-driven decision making solution which takes both agricultural studies and applied machine learning into account.

This is an ensemble ML model developed to predict corn yield for 12 states of the US Corn Belt. We have combined outputs from crop simulation model with ML models to build the prediction model.

MLCAS Corn Yield Prediction Using Satellite Data model

Stacking method for corn yield and EONR prediction

A study that uses statistical, machine learning and deep learning models to predict corn yield in Adana Province

Hackation IoT4Ag 2025

Overview of ML research project @ NMSP (Cornell)

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This project using various machine learning models and advanced feature engineering. The dataset includes meteorological variables and corn yield data collected over multiple years.

Built a regression model to predict corn yield using polynomial regression, capturing non-linear relationships to improve prediction accuracy.

Data analysis and machine learning models to predict corn yield based on soil, climate, and fertilizer data (University TD project).

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Illinois corn yield prediction project as part of undergraduate research in UCLA's Karen McKinnon lab.

Machine learning prediction of U.S. corn yield using climate data.

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