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    Prediction of Photosynthetic Carbon Assimilation Rate of Individual Rice Leaves under Changes in Light Environment Using BLSTM-Augmented LSTM

    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… More >

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