This study analyzed the performance of different machine learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. To address the seasonality, weekly features were used that explicitly take soil moisture conditions and meteorological events into account. Our results indicated that nonlinear models such as deep neural networks (DNN) and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models. The results also revealed that the deep neural networks often had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. As a result, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). The feature selection method estimated the individual effect of weather components, soil conditions, and phenology variables as well as the time that these variables become important. As such, our study indicates which variables have the most significant effect on winter wheat yield.
翻译:这项研究利用天气、土壤和作物动物学的广泛数据集分析了冬季小麦产量预测的不同机器学习方法的性能。为了解决季节性问题,使用了明确考虑到土壤湿度条件和气象事件的每周特征。我们的结果表明,深神经网络(DNN)和XGbousst等非线性模型在寻找作物产量和输入数据之间的功能关系方面比线性模型更为有效。结果还表明,深神经网络的预测准确性往往高于XGboost。机器学习模型的主要局限性之一是它们的黑盒属性。结果,我们超越了预测,进行了特征选择,因为它为解释产量预测提供了关键结果(随着时间的推移具有不同的重要性 ) 。 特征选择方法估计了天气组成部分、土壤条件和植物变量的个别影响以及这些变量变得重要的时间。因此,我们的研究指出了哪些变量对冬季小麦产量具有最显著的影响。