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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

Cited 1 time in Web of Science Cited 1 time in Scopus
Authors
Moon, Taewon; Ahn, Tae In; Son, Jung Eek
Issue Date
2019-05-28
Publisher
BioMed Central
Citation
Plant Methods. 15(1):59
Keywords
Environmental factorHydroponicsMachine learningModel-free estimationPaprika
Abstract
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 R2 = 0.84 and the test accuracy was R2 = 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.
ISSN
1746-4811
Language
English
URI
http://hdl.handle.net/10371/156039
DOI
https://doi.org/10.1186/s13007-019-0443-7
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College of Agriculture and Life Sciences (농업생명과학대학)Dept. of Plant Science (식물생산과학부)Journal Papers (저널논문_식물생산과학부)
College of Agriculture and Life Sciences (농업생명과학대학)Research Institute of Agriculture and Life Sciences (농업생명과학연구원)Journal Papers (저널논문_농업생명과학연구원)
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