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Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures
BACKGROUND: Root-zone environment is considered difficult to analyze, particularly in interpreting interactions between environment and plant. Closed-loop soilless cultures have been introduced to prevent environmental pollution, but difficulties in managing nutrients can cause nutrient imbalances w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540585/ https://www.ncbi.nlm.nih.gov/pubmed/31160918 http://dx.doi.org/10.1186/s13007-019-0443-7 |
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author | Moon, Taewon Ahn, Tae In Son, Jung Eek |
author_facet | Moon, Taewon Ahn, Tae In Son, Jung Eek |
author_sort | Moon, Taewon |
collection | PubMed |
description | BACKGROUND: Root-zone environment is considered difficult to analyze, particularly in interpreting interactions between environment and plant. Closed-loop soilless cultures have been introduced to prevent environmental pollution, but difficulties in managing nutrients can cause nutrient imbalances with an adverse effect on crop growth. Recently, deep learning has been used to draw meaningful results from nonlinear data and long short-term memory (LSTM) is showing state-of-the-art results in analyzing time-series data. Therefore the macronutrient ion concentrations affected by accumulated environment conditions can be analyzed using LSTM. RESULTS: The trained LSTM can estimate macronutrient ion concentrations in closed-loop soilless cultures using environmental and growth data. The average training accuracy of six macronutrients was R(2) = 0.84 and the test accuracy was R(2) = 0.67 with RMSE = 1.48 meq L(−1). The used values of input interval and time step were 1 h and 168 (1 week), respectively. The accuracy was improved when the input interval became shorter, but not improved when the LSTM consisted of a multilayer structure. Regarding training methods, the LSTM improved the accuracy better than the non-LSTM. The trained LSTM showed relatively adequate accuracies and the interpolated ion concentrations showed variations similar to those seen during traditional cultivation. CONCLUSIONS: We could analyze the nutrient balance in the closed-loop soilless culture, the model showed potential in estimating the macronutrient ion concentrations using environmental and growth factors measured in greenhouses. Since the LSTM is a powerful and flexible tool used to interpret accumulative changes, it is easily applicable to various plant and cultivation conditions. In the future, this approach can be used to analyze interactions between plant physiology and root-zone environment. |
format | Online Article Text |
id | pubmed-6540585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65405852019-06-03 Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures Moon, Taewon Ahn, Tae In Son, Jung Eek Plant Methods Methodology BACKGROUND: Root-zone environment is considered difficult to analyze, particularly in interpreting interactions between environment and plant. Closed-loop soilless cultures have been introduced to prevent environmental pollution, but difficulties in managing nutrients can cause nutrient imbalances with an adverse effect on crop growth. Recently, deep learning has been used to draw meaningful results from nonlinear data and long short-term memory (LSTM) is showing state-of-the-art results in analyzing time-series data. Therefore the macronutrient ion concentrations affected by accumulated environment conditions can be analyzed using LSTM. RESULTS: The trained LSTM can estimate macronutrient ion concentrations in closed-loop soilless cultures using environmental and growth data. The average training accuracy of six macronutrients was R(2) = 0.84 and the test accuracy was R(2) = 0.67 with RMSE = 1.48 meq L(−1). The used values of input interval and time step were 1 h and 168 (1 week), respectively. The accuracy was improved when the input interval became shorter, but not improved when the LSTM consisted of a multilayer structure. Regarding training methods, the LSTM improved the accuracy better than the non-LSTM. The trained LSTM showed relatively adequate accuracies and the interpolated ion concentrations showed variations similar to those seen during traditional cultivation. CONCLUSIONS: We could analyze the nutrient balance in the closed-loop soilless culture, the model showed potential in estimating the macronutrient ion concentrations using environmental and growth factors measured in greenhouses. Since the LSTM is a powerful and flexible tool used to interpret accumulative changes, it is easily applicable to various plant and cultivation conditions. In the future, this approach can be used to analyze interactions between plant physiology and root-zone environment. BioMed Central 2019-05-28 /pmc/articles/PMC6540585/ /pubmed/31160918 http://dx.doi.org/10.1186/s13007-019-0443-7 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Moon, Taewon Ahn, Tae In Son, Jung Eek Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures |
title | Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures |
title_full | Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures |
title_fullStr | Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures |
title_full_unstemmed | Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures |
title_short | Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures |
title_sort | long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540585/ https://www.ncbi.nlm.nih.gov/pubmed/31160918 http://dx.doi.org/10.1186/s13007-019-0443-7 |
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