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Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM

BACKGROUND: Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-lea...

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Autores principales: Nie, Jing, Wang, Nianyi, Li, Jingbin, Wang, Kang, Wang, Hongkun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611850/
https://www.ncbi.nlm.nih.gov/pubmed/34819082
http://dx.doi.org/10.1186/s13007-021-00818-2
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author Nie, Jing
Wang, Nianyi
Li, Jingbin
Wang, Kang
Wang, Hongkun
author_facet Nie, Jing
Wang, Nianyi
Li, Jingbin
Wang, Kang
Wang, Hongkun
author_sort Nie, Jing
collection PubMed
description BACKGROUND: Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. METHOD: In this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML’s gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness. RESULTS: The average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model. The loss value of the meta-learning optimization model in the iterative process drops the fastest and steadily compared to the MAML model and the LSTM model. In cross-domain experiments, the average accuracy of the meta-learning optimized model can still reach 0.833. CONCLUSIONS: In the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model. And in the training of cross-domain datasets, this model performs best.
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spelling pubmed-86118502021-11-29 Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM Nie, Jing Wang, Nianyi Li, Jingbin Wang, Kang Wang, Hongkun Plant Methods Research BACKGROUND: Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. METHOD: In this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML’s gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness. RESULTS: The average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model. The loss value of the meta-learning optimization model in the iterative process drops the fastest and steadily compared to the MAML model and the LSTM model. In cross-domain experiments, the average accuracy of the meta-learning optimized model can still reach 0.833. CONCLUSIONS: In the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model. And in the training of cross-domain datasets, this model performs best. BioMed Central 2021-11-24 /pmc/articles/PMC8611850/ /pubmed/34819082 http://dx.doi.org/10.1186/s13007-021-00818-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Nie, Jing
Wang, Nianyi
Li, Jingbin
Wang, Kang
Wang, Hongkun
Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM
title Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM
title_full Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM
title_fullStr Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM
title_full_unstemmed Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM
title_short Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM
title_sort meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on lstm
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611850/
https://www.ncbi.nlm.nih.gov/pubmed/34819082
http://dx.doi.org/10.1186/s13007-021-00818-2
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