Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), mo...

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Autores principales:	Xuelian Peng, Xiaotao Hu, Dianyu Chen, Zhenjiang Zhou, Yinyin Guo, Xin Deng, Xingguo Zhang, Tinggao Yu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	greenhouse grapes sap flow rate random forest model meteorology soil moisture uncertainty analysis Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/c93679d2cab74aefaa6e2383447448e3
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spelling	oai:doaj.org-article:c93679d2cab74aefaa6e2383447448e32021-11-11T19:56:42ZPrediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models10.3390/w132130782073-4441https://doaj.org/article/c93679d2cab74aefaa6e2383447448e32021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3078https://doaj.org/toc/2073-4441Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation.Xuelian PengXiaotao HuDianyu ChenZhenjiang ZhouYinyin GuoXin DengXingguo ZhangTinggao YuMDPI AGarticlegreenhouse grapessap flow raterandom forest modelmeteorologysoil moistureuncertainty analysisHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3078, p 3078 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	greenhouse grapes sap flow rate random forest model meteorology soil moisture uncertainty analysis Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	greenhouse grapes sap flow rate random forest model meteorology soil moisture uncertainty analysis Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Xuelian Peng Xiaotao Hu Dianyu Chen Zhenjiang Zhou Yinyin Guo Xin Deng Xingguo Zhang Tinggao Yu Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models
description	Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation.
format	article
author	Xuelian Peng Xiaotao Hu Dianyu Chen Zhenjiang Zhou Yinyin Guo Xin Deng Xingguo Zhang Tinggao Yu
author_facet	Xuelian Peng Xiaotao Hu Dianyu Chen Zhenjiang Zhou Yinyin Guo Xin Deng Xingguo Zhang Tinggao Yu
author_sort	Xuelian Peng
title	Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models
title_short	Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models
title_full	Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models
title_fullStr	Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models
title_full_unstemmed	Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models
title_sort	prediction of grape sap flow in a greenhouse based on random forest and partial least squares models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c93679d2cab74aefaa6e2383447448e3
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Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

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