Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse

The sap flow of plants directly indicates their water requirements and provides farmers with a good understanding of a plant’s water consumption. Water management can be improved based on this information. This study focuses on forecasting tomato sap flow in relation to various climate an...

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Autores principales:	Amora Amir, Marya Butt, Olaf Van Kooten
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Sap flow tomato future forecasting machine learning feature importance hyperparameters Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d788b6570f1d4e51951d32cdc4d2fab0
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id	oai:doaj.org-article:d788b6570f1d4e51951d32cdc4d2fab0
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spelling	oai:doaj.org-article:d788b6570f1d4e51951d32cdc4d2fab02021-11-24T00:02:59ZUsing Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse2169-353610.1109/ACCESS.2021.3127453https://doaj.org/article/d788b6570f1d4e51951d32cdc4d2fab02021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9611229/https://doaj.org/toc/2169-3536The sap flow of plants directly indicates their water requirements and provides farmers with a good understanding of a plant’s water consumption. Water management can be improved based on this information. This study focuses on forecasting tomato sap flow in relation to various climate and irrigation variables. The proposed study utilizes different machine learning (ML) techniques, including linear regression (LR), least absolute shrinkage and selection operator (LASSO), elastic net regression (ENR), support vector regression (SVR), random forest (RF), gradient boosting (GB) and decision tree (DT). The forecasting performance of different ML techniques is evaluated. The results show that RF offers the best performance in predicting sap flow. SVR performs poorly in this study. Given water/m<sup>2</sup>, room temperature, given water EC, humidity and plant temperature are the best predictors of sap flow. The data are obtained from the Ideal Lab greenhouse, in the Netherlands, in the framework of the European Funds for Regionale Ontwikkeling (EFRO) EVERGREEN Greenport Noord Holland Noord project (2018-2020).Amora AmirMarya ButtOlaf Van KootenIEEEarticleSap flowtomatofuture forecastingmachine learningfeature importancehyperparametersElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154183-154193 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sap flow tomato future forecasting machine learning feature importance hyperparameters Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Sap flow tomato future forecasting machine learning feature importance hyperparameters Electrical engineering. Electronics. Nuclear engineering TK1-9971 Amora Amir Marya Butt Olaf Van Kooten Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse
description	The sap flow of plants directly indicates their water requirements and provides farmers with a good understanding of a plant’s water consumption. Water management can be improved based on this information. This study focuses on forecasting tomato sap flow in relation to various climate and irrigation variables. The proposed study utilizes different machine learning (ML) techniques, including linear regression (LR), least absolute shrinkage and selection operator (LASSO), elastic net regression (ENR), support vector regression (SVR), random forest (RF), gradient boosting (GB) and decision tree (DT). The forecasting performance of different ML techniques is evaluated. The results show that RF offers the best performance in predicting sap flow. SVR performs poorly in this study. Given water/m<sup>2</sup>, room temperature, given water EC, humidity and plant temperature are the best predictors of sap flow. The data are obtained from the Ideal Lab greenhouse, in the Netherlands, in the framework of the European Funds for Regionale Ontwikkeling (EFRO) EVERGREEN Greenport Noord Holland Noord project (2018-2020).
format	article
author	Amora Amir Marya Butt Olaf Van Kooten
author_facet	Amora Amir Marya Butt Olaf Van Kooten
author_sort	Amora Amir
title	Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse
title_short	Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse
title_full	Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse
title_fullStr	Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse
title_full_unstemmed	Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse
title_sort	using machine learning algorithms to forecast the sap flow of cherry tomatoes in a greenhouse
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/d788b6570f1d4e51951d32cdc4d2fab0
work_keys_str_mv	AT amoraamir usingmachinelearningalgorithmstoforecastthesapflowofcherrytomatoesinagreenhouse AT maryabutt usingmachinelearningalgorithmstoforecastthesapflowofcherrytomatoesinagreenhouse AT olafvankooten usingmachinelearningalgorithmstoforecastthesapflowofcherrytomatoesinagreenhouse
_version_	1718416123790098432

Using Machine Learning Algorithms to Forecast the Sap Flow of Cherry Tomatoes in a Greenhouse

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