Crop yield prediction typically involves the utilization of either theory-driven process-based crop growth models, which have proven to be difficult to calibrate for local conditions, or data-driven machine learning methods, which are known to require large datasets. In this work we investigate potato yield prediction using a hybrid meta-modeling approach. A crop growth model is employed to generate synthetic data for (pre)training a convolutional neural net, which is then fine-tuned with observational data. When applied in silico, our meta-modeling approach yields better predictions than a baseline comprising a purely data-driven approach. When tested on real-world data from field trials (n=303) and commercial fields (n=77), the meta-modeling approach yields competitive results with respect to the crop growth model. In the latter set, however, both models perform worse than a simple linear regression with a hand-picked feature set and dedicated preprocessing designed by domain experts. Our findings indicate the potential of meta-modeling for accurate crop yield prediction; however, further advancements and validation using extensive real-world datasets is recommended to solidify its practical effectiveness.

翻译：作物产量预测通常涉及两种方法：一是基于理论驱动的过程式作物生长模型，这类模型难以针对当地条件进行校准；二是数据驱动的机器学习方法，这类方法需要大量数据集。本研究采用混合元建模方法探究马铃薯产量预测。我们利用作物生长模型生成合成数据，用于（预）训练卷积神经网络，随后利用观测数据对其进行微调。在计算机模拟应用中，我们的元建模方法比纯数据驱动基线方法取得了更优的预测效果。当对田间试验（n=303）和商业农田（n=77）的真实世界数据进行测试时，该元建模方法在性能上与作物生长模型不相上下。然而在后一组数据中，两种模型的表现均不如领域专家通过手工选取特征集并设计专用预处理方法构建的简单线性回归模型。研究结果表明元建模在精准作物产量预测方面具有潜力，但建议通过大规模真实世界数据集进行进一步改进与验证，以夯实其实践有效性。