Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques

Abstract Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root...

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Autores principales:	Doudou Guo, Jiaxiang Juan, Liying Chang, Jingjin Zhang, Danfeng Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a649e22387c54bcaa8eb6c90b0749085
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spelling	oai:doaj.org-article:a649e22387c54bcaa8eb6c90b07490852021-12-02T12:32:42ZDiscrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques10.1038/s41598-017-08235-z2045-2322https://doaj.org/article/a649e22387c54bcaa8eb6c90b07490852017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08235-zhttps://doaj.org/toc/2045-2322Abstract Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.Doudou GuoJiaxiang JuanLiying ChangJingjin ZhangDanfeng HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Doudou Guo Jiaxiang Juan Liying Chang Jingjin Zhang Danfeng Huang Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
description	Abstract Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.
format	article
author	Doudou Guo Jiaxiang Juan Liying Chang Jingjin Zhang Danfeng Huang
author_facet	Doudou Guo Jiaxiang Juan Liying Chang Jingjin Zhang Danfeng Huang
author_sort	Doudou Guo
title	Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
title_short	Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
title_full	Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
title_fullStr	Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
title_full_unstemmed	Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
title_sort	discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a649e22387c54bcaa8eb6c90b0749085
work_keys_str_mv	AT doudouguo discriminationofplantrootzonewaterstatusingreenhouseproductionbasedonphenotypingandmachinelearningtechniques AT jiaxiangjuan discriminationofplantrootzonewaterstatusingreenhouseproductionbasedonphenotypingandmachinelearningtechniques AT liyingchang discriminationofplantrootzonewaterstatusingreenhouseproductionbasedonphenotypingandmachinelearningtechniques AT jingjinzhang discriminationofplantrootzonewaterstatusingreenhouseproductionbasedonphenotypingandmachinelearningtechniques AT danfenghuang discriminationofplantrootzonewaterstatusingreenhouseproductionbasedonphenotypingandmachinelearningtechniques
_version_	1718393997714522112

Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques

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