Abstract

Accurate prediction of crop yield before harvest is critical to food security and importation. The calculated ten explanatory factors and autumn crop yield data were used as data sources in this research. Firstly, a Redundancy Analysis (RDA) was employed to carry out explanatory factors and feature selection. The simple effects of RDA were used to evaluate the interpretation rates of the explanatory factors. The conditional effects of RDA were adopted to select the features of the explanatory factors. Then, the autumn crop yield was divided into the training set and testing set with an 80/20 ratio, using Support Vector Regression (SVR), Random Forest Regression (RFR), and deep neural network (DNN) for the model, respectively. Finally, the coefficient of determination (R²), the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) were used to evaluate the performance of the model comprehensively. The results showed that the interpretation rates of the explanatory factors ranged from 54.3% to 85.0% (p = 0.002), which could reflect the autumn crop yields well. When a small number of sample training data (e.g., 80 samples) was used, the DNN model performed better than both SVR and RF models.

摘要

在收获前准确预测作物产量对粮食安全和进口至关重要。计算出的十个解释因子和秋季作物产量数据作为本研究的数据来源。首先，采用冗余分析（RDA）进行解释因素和特征选择。RDA 的简单效应被用来评估解释因素的解释率。采用RDA的条件效应来选择解释因素的特征。然后，分别使用支持向量回归（SVR）、随机森林回归（RFR）和深度神经网络（DNN）作为模型，将秋季作物产量以80/20的比例划分为训练集和测试集。最后，决定系数（R ²)、均方根误差(RMSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)综合评价模型的性能。结果表明，解释因子的解释率为54.3%～85.0%（p  =0.002），较好地反映了秋季作物产量。当使用少量样本训练数据（例如，80 个样本）时，DNN 模型的性能优于 SVR 和 RF 模型。