A New Framework for Winter Wheat Yield Prediction Integrating Deep Learning and Bayesian Optimization

文献类型： 外文期刊

第一作者： Di, Yan

作者： Di, Yan;Gao, Maofang;Feng, Fukang;Li, Qiang;Di, Yan;Gao, Maofang;Feng, Fukang;Li, Qiang;Zhang, Huijie

作者机构：

关键词： yield prediction; deep learning; BO-LSTM; remote sensing; meteorological data

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 12 期

页码：

收录情况： SCI

摘要： Early prediction of winter wheat yield at the regional scale is essential for food policy making and food security, especially in the context of population growth and climate change. Agricultural big data and artificial intelligence (AI) are key technologies for smart agriculture, bringing cost-effective solutions to the agricultural sector. Deep learning-based crop yield forecast has currently emerged as one of the key methods for guiding agricultural production. In this study, we proposed a Bayesian optimization-based long- and short-term memory model (BO-LSTM) to construct a multi-source data fusion-driven crop growth feature extraction algorithm for winter wheat yield prediction. The yield prediction performance of BO-LSTM, support vector machine (SVM), and least absolute shrinkage and selection operator (Lasso) was then compared with multi-source data as input variables. The results showed that effective deep learning hyperparameter optimization is made possible by Bayesian optimization. The BO-LSTM (RMSE = 177.84 kg/ha, R-2 = 0.82) model had the highest accuracy of yield prediction with the input combination of "GPP + Climate + LAI + VIs". BO-LSTM and SVM (RMSE = 185.7 kg/ha, R-2 = 0.80) methods outperformed linear regression Lasso (RMSE = 214.5 kg/ha, R-2 = 0.76) for winter wheat yield estimation. There were also differences between machine learning and deep learning, BO-LSTM outperformed SVM. indicating that the BO-LSTM model was more effective at capturing data correlations. In order to further verify the robustness of the BO-LSTM method, we explored the performance estimation performance of BO-LSTM in different regions. The results demonstrated that the BO-LSTM model could obtain higher estimation accuracy in regions with concentrated distribution of winter wheat cultivation and less influence of human factors. The approach used in this study can be expected to forecast crop yields, both in regions with a deficit of data and globally; it can also simply and effectively forecast winter wheat yields in a timely way utilizing publicly available multi-source data.

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