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

Abstract 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...

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Autores principales:	Jing Nie, Nianyi Wang, Jingbin Li, Kang Wang, Hongkun Wang
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Meta-learning Regression prediction Meta-learner LSTM MAML Plant culture SB1-1110 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6b9f7a9f7272482e9f3561aa2b7cb0d5
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spelling	oai:doaj.org-article:6b9f7a9f7272482e9f3561aa2b7cb0d52021-11-28T12:09:15ZMeta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM10.1186/s13007-021-00818-21746-4811https://doaj.org/article/6b9f7a9f7272482e9f3561aa2b7cb0d52021-11-01T00:00:00Zhttps://doi.org/10.1186/s13007-021-00818-2https://doaj.org/toc/1746-4811Abstract 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.Jing NieNianyi WangJingbin LiKang WangHongkun WangBMCarticleMeta-learningRegression predictionMeta-learner LSTMMAMLPlant cultureSB1-1110Biology (General)QH301-705.5ENPlant Methods, Vol 17, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Meta-learning Regression prediction Meta-learner LSTM MAML Plant culture SB1-1110 Biology (General) QH301-705.5
spellingShingle	Meta-learning Regression prediction Meta-learner LSTM MAML Plant culture SB1-1110 Biology (General) QH301-705.5 Jing Nie Nianyi Wang Jingbin Li Kang Wang Hongkun Wang Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM
description	Abstract 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.
format	article
author	Jing Nie Nianyi Wang Jingbin Li Kang Wang Hongkun Wang
author_facet	Jing Nie Nianyi Wang Jingbin Li Kang Wang Hongkun Wang
author_sort	Jing Nie
title	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_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_sort	meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on lstm
publisher	BMC
publishDate	2021
url	https://doaj.org/article/6b9f7a9f7272482e9f3561aa2b7cb0d5
work_keys_str_mv	AT jingnie metalearningpredictionofphysicalandchemicalpropertiesofmagnetizedwaterandfertilizerbasedonlstm AT nianyiwang metalearningpredictionofphysicalandchemicalpropertiesofmagnetizedwaterandfertilizerbasedonlstm AT jingbinli metalearningpredictionofphysicalandchemicalpropertiesofmagnetizedwaterandfertilizerbasedonlstm AT kangwang metalearningpredictionofphysicalandchemicalpropertiesofmagnetizedwaterandfertilizerbasedonlstm AT hongkunwang metalearningpredictionofphysicalandchemicalpropertiesofmagnetizedwaterandfertilizerbasedonlstm
_version_	1718408144793632768

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

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