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.

本文提出了一个综合的方法，将数据驱动解决方案与作物模拟模型（CSMs）和模型替代品相结合，以支持农作物管理决策时的多个用户配置文件和需求，我们的数据驱动建模方法与作物产量的相关性预测接近91％，而作物模拟建模结构实现了6％的误差；所提出的作物模拟模型替代品的精度水平与采用的作物模拟器相似，但执行速度快了近100倍

基于 AI方法和农作物模拟模型的作物收成预测的全面建模方法