Machine learning-based analysis of nutrient and water uptake in hydroponically grown soybeans.
Hydroponics
Machine learning
Non-parametric regression
Shapley additive explanations
Sustainable agriculture
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 Oct 2024
17 Oct 2024
Historique:
received:
18
12
2023
accepted:
24
09
2024
medline:
18
10
2024
pubmed:
18
10
2024
entrez:
17
10
2024
Statut:
epublish
Résumé
Recent advancements in sustainable agriculture have spurred interest in hydroponics as an alternative to conventional farming methods. However, the lack of data-driven approaches in hydroponic growth presents a significant challenge. This study addresses this gap by varying nitrogen, magnesium, and potassium concentrations in hydroponically grown soybeans and conducting essential nutrient profiling across the growth cycle. Statistical techniques like Linear Interpolation are employed to interpolate nutrient data and a feature selection pipeline consisting of chi-squared testing methods, Linear Regression with Recursive Feature Elimination (RFE) and ExtraTreesClassifier have been used to select important nutrients for predicting water uptake using non-parametric regression methods. For different nutrient growth media, i.e. for soybeans grown in Hoagland + Nitrogen and Hoagland + Magnesium media, the Random Forest regressor outperformed other methods in predicting water uptake, achieving testing Mean Squared Error (MSE) scores of 24.55 (
Identifiants
pubmed: 39420136
doi: 10.1038/s41598-024-74376-7
pii: 10.1038/s41598-024-74376-7
doi:
Substances chimiques
Water
059QF0KO0R
Nitrogen
N762921K75
Potassium
RWP5GA015D
Magnesium
I38ZP9992A
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24337Informations de copyright
© 2024. The Author(s).
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