A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon

Cultivation substrate water status is of great importance to the production of netted muskmelon (<i>Cucumis melo</i> L. var. reticulatus Naud.). A prediction model for the substrate water status would be beneficial in irrigation schedule guidance. In this study, the machine learning rand...

Full description

Bibliographic Details
Main Authors:	Liying Chang, Yilu Yin, Jialin Xiang, Qian Liu, Daren Li, Danfeng Huang
Format:	Article
Language:	English
Published:	MDPI AG 2019-06-01
Series:	Sensors
Subjects:	muskmelon phenotype random forest algorithm cultivation substrate water status forecasting
Online Access:	https://www.mdpi.com/1424-8220/19/12/2673

id	doaj-834d4c6f01d3490b957f260cdbd22865
record_format	Article
spelling	doaj-834d4c6f01d3490b957f260cdbd228652020-11-25T01:50:53ZengMDPI AGSensors1424-82202019-06-011912267310.3390/s19122673s19122673A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted MuskmelonLiying Chang0Yilu Yin1Jialin Xiang2Qian Liu3Daren Li4Danfeng Huang5School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, ChinaCultivation substrate water status is of great importance to the production of netted muskmelon (<i>Cucumis melo</i> L. var. reticulatus Naud.). A prediction model for the substrate water status would be beneficial in irrigation schedule guidance. In this study, the machine learning random forest model was used to forecast plant substrate water status given the phenotypic traits throughout the muskmelon growing season. Here, two varieties of netted muskmelon, “Wanglu” and “Arus”, were planted in a greenhouse under four substrate water treatments and their phenotypic traits were measured by taking the images within the visible and near-infrared spectrums, respectively. Results showed that a simplified model outperformed the original model in forecasting speed, while it only uses the top five most significant contribution traits. The forecast accuracy reached up to 77.60%, 94.37%, and 90.01% for seedling, vine elongation, and fruit growth stages, respectively. Combining the imaging phenotypic traits and machine learning technique would provide a robust forecast of water status around the plant root zones.https://www.mdpi.com/1424-8220/19/12/2673muskmelonphenotyperandom forest algorithmcultivation substrate water statusforecasting
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Liying Chang Yilu Yin Jialin Xiang Qian Liu Daren Li Danfeng Huang
spellingShingle	Liying Chang Yilu Yin Jialin Xiang Qian Liu Daren Li Danfeng Huang A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon Sensors muskmelon phenotype random forest algorithm cultivation substrate water status forecasting
author_facet	Liying Chang Yilu Yin Jialin Xiang Qian Liu Daren Li Danfeng Huang
author_sort	Liying Chang
title	A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon
title_short	A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon
title_full	A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon
title_fullStr	A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon
title_full_unstemmed	A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon
title_sort	phenotype-based approach for the substrate water status forecast of greenhouse netted muskmelon
publisher	MDPI AG
series	Sensors
issn	1424-8220
publishDate	2019-06-01
description	Cultivation substrate water status is of great importance to the production of netted muskmelon (<i>Cucumis melo</i> L. var. reticulatus Naud.). A prediction model for the substrate water status would be beneficial in irrigation schedule guidance. In this study, the machine learning random forest model was used to forecast plant substrate water status given the phenotypic traits throughout the muskmelon growing season. Here, two varieties of netted muskmelon, “Wanglu” and “Arus”, were planted in a greenhouse under four substrate water treatments and their phenotypic traits were measured by taking the images within the visible and near-infrared spectrums, respectively. Results showed that a simplified model outperformed the original model in forecasting speed, while it only uses the top five most significant contribution traits. The forecast accuracy reached up to 77.60%, 94.37%, and 90.01% for seedling, vine elongation, and fruit growth stages, respectively. Combining the imaging phenotypic traits and machine learning technique would provide a robust forecast of water status around the plant root zones.
topic	muskmelon phenotype random forest algorithm cultivation substrate water status forecasting
url	https://www.mdpi.com/1424-8220/19/12/2673
work_keys_str_mv	AT liyingchang aphenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT yiluyin aphenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT jialinxiang aphenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT qianliu aphenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT darenli aphenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT danfenghuang aphenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT liyingchang phenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT yiluyin phenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT jialinxiang phenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT qianliu phenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT darenli phenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon AT danfenghuang phenotypebasedapproachforthesubstratewaterstatusforecastofgreenhousenettedmuskmelon
_version_	1724999935839961088

A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon

Similar Items