Masayuki Honda1, Kenichi Tatsumi2,*, Masaki Nakagawa3
CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 557-577, 2022, DOI:10.32604/cmes.2022.020623
Abstract A model to predict photosynthetic carbon assimilation rate (A) with high accuracy is important for forecasting
crop yield and productivity. Long short-term memory (LSTM), a neural network suitable for time-series data,
enables prediction with high accuracy but requires mesophyll variables. In addition, for practical use, it is desirable
to have a technique that can predict A from easily available information. In this study, we propose a BLSTMaugmented LSTM (BALSTM) model, which utilizes bi-directional LSTM (BLSTM) to indirectly reproduce the
mesophyll variables required for LSTM. The most significant feature of the proposed model is that its hybrid
architecture uses only three… More >
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