A Stacking Ensemble Learning Model Combining a Crop Simulation Model with Machine Learning to Improve the Dry Matter Yield Estimation of Greenhouse Pakchoi

Abstract

Crop models are instrumental in simulating resource utilization in agriculture, yet their complexity necessitates extensive calibration, which can impact the accuracy of yield predictions. Machine learning shows promise for enhancing yield estimations but relies on vast amounts of training data. This study aims to improve the pakchoi yield prediction accuracy of simulation models. We developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction. To enhance the training dataset and bolster machine learning performance, we employed the EU-Rotate_N model to simulate daily dry matter yields for unsampled data. The test results revealed that the stacking model outperformed each base model. The stacking model achieved an R² value of 0.834, which was approximately 0.1 higher than that of the EU-Rotate_N model. The RMSE and MAE were 0.283 t/ha and 0.196 t/ha, respectively, both approximately 0.6 t/ha lower than those of the EU-Rotate_N model. The performance of the stacking model, developed with the expanded dataset, showed a significant improvement over the model based on the original dataset.

Plant phenotyping relevance

パクチョイの乾物収量という植物形質を推定するスタッキング予測モデルを開発し、複数モデルとの性能比較・検証を行っており、形質推定手法が研究の中心である。

Code and data availability

The supplied blocks describe a greenhouse pakchoi experiment and a stacking ensemble model, but contain no data availability statement, repository deposit, or public URL for the phenotype datasets, EU-Rotate_N simulation outputs, or analysis code. No paper-specific public asset is identified.

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