Prediction of Rice Yield via Stacked LSTM
Muyan Liu and
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Xiangyan Meng: College of Science, Northeast Agricultural University, Harbin, China
Muyan Liu: College of Engineering, Northeast Agricultural University, Harbin, China
Qiufeng Wu: College of Science, Northeast Agricultural University, Harbin, China
International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2020, vol. 11, issue 1, 86-95
In order to guarantee the rice yield more effectively, the prediction of rice yield should be taken into account. Because the rice yield every year can be seen as a sequence of time series, many methods applied in prediction of time series can be considered. Long Short-Term Memory recurrent neural network (LSTM) is one of the most popular methods of time series prediction. In consideration of its own characteristics and the popularity of deep learning, an improved LSTM architecture called Stacked LSTM which has multiple layers is proposed in this article. It is based on the idea of increasing the depth of LSTM. The comparison among the Stacked LSTM architectures which have different numbers of LSTM layers and other methods including ARIMA, GRU, and ANN has been carried out on the data of rice yield in Heilongjiang Province, China, from 1980 to 2017. The results showed the superior performance of Stacked LSTM and the effectiveness of increasing the depth of LSTM.
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