A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models

TL;DR

This paper presents an integrated AI and crop simulation modeling framework for crop yield forecasting, combining data-driven models, scalable crop simulation calibration, and surrogate models for faster, accurate predictions across diverse user needs.

Contribution

It introduces a comprehensive approach that combines data-driven models, scalable crop simulation calibration, and surrogate modeling for efficient, accurate crop yield forecasting.

Findings

Yield prediction correlation of 91%

Crop simulation error of 6%

Surrogate model 100x faster than traditional CSMs

Abstract

Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the USDA. On the opposite side of the spectrum, CSMs require fine data that may be hard to generalize from a handful of fields. In this paper, we propose a comprehensive approach for yield forecasting that combines data-driven solutions, crop simulation models, and model surrogates to support multiple user-profiles and needs when dealing with crop management decision-making. To achieve this goal, we have developed a solution to calibrate CSMs at scale, a surrogate model of a CSM assuring faster execution, and a neural network-based approach that performs efficient risk assessment in such settings. Our data-driven modeling approach outperforms previous works with yield correlation predictions close to 91\%. The crop simulation modeling architecture achieved 6% error; the proposed crop simulation model surrogate performs predictions almost 100 times faster than the adopted crop simulator with similar accuracy levels.