Autumn Crop Yield Prediction using Data-Driven Approaches:- Support Vector Machines, Random Forest, and Deep Neural Network Methods

摘要

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 sourcesin this research. Firstly, a Redundancy Analysis (RDA) was employed to carry outexplanatory factors and feature selection. The simple effects of RDA were used to evaluatethe interpretation rates of the explanatory factors. The conditional effects of RDA wereadopted to select the features of the explanatory factors. Then, the autumn crop yield wasdivided into the training set and testing set with an 80/20 ratio, using Support VectorRegression (SVR), Random Forest Regression (RFR), and deep neural network (DNN) for themodel, 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 showedthat 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 sampletraining data (e.g., 80 samples) was used, the DNN model performed better than bothSVR and RF models.