干旱区生态环境知识资源中心(Arid): Crop-water assessment in Citrus (Citrus sinensis L.) based on continuous measurements of leaf-turgor pressure using machine learning and IoT

Arid

DOI	10.1016/j.eswa.2022.118255
	Crop-water assessment in Citrus (Citrus sinensis L.) based on continuous measurements of leaf-turgor pressure using machine learning and IoT
	Barriga, Jose A.; Blanco-Cipollone, Fernando; Trigo-Cordoba, Emiliano; Garcia-Tejero, Ivan; Clemente, Pedro J.
通讯作者	Barriga, JA ; Clemente, PJ
来源期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
EISSN	1873-6793
出版年	2022
卷号	209
英文摘要	Water is the most limiting natural resource in many semi-arid areas. This, together with the current climate change scenario, is fostering a context of uncertainty and major challenges concerning the sustainability and viability of existing agroecosystems. Crop water status based on three pre-established values (severe, mild, and no stress) is the essential datum needed to implement optimised irrigation scheduling based on deficit irrigation. Currently however, its calculation is a repetitive, tedious, and technical process carried out by hand. This communication presents a novel system based on continuous measurements of leaf turgor pressure to assess the crop water status when deficit irrigation strategies are being applied and/or to optimise irrigation scheduling in water scarcity scenarios. To this end, a novel expert system based on machine learning, together with an IoT infrastructure based on continuous measurements of leaf turgor pressure, is able to predict the citrus crop.......... with a 99% F1 score. Thus, crop irrigation strategies involving irrigation-restriction cycles can be applied based on stem water potential.
英文关键词	IoT Machine learning Expert system Turgor pressure Stem water potential Irrigation scheduling
类型	Review
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000888799700002
WOS关键词	DEFICIT IRRIGATION ; FEATURE-SELECTION ; FRUIT-QUALITY ; MANAGEMENT ; IMPACT ; TEMPERATURE ; PREDICTION ; SHRINKAGE ; EQUATIONS ; STRESS
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392570
推荐引用方式 GB/T 7714	Barriga, Jose A.,Blanco-Cipollone, Fernando,Trigo-Cordoba, Emiliano,et al. Crop-water assessment in Citrus (Citrus sinensis L.) based on continuous measurements of leaf-turgor pressure using machine learning and IoT[J],2022,209.
APA	Barriga, Jose A.,Blanco-Cipollone, Fernando,Trigo-Cordoba, Emiliano,Garcia-Tejero, Ivan,&Clemente, Pedro J..(2022).Crop-water assessment in Citrus (Citrus sinensis L.) based on continuous measurements of leaf-turgor pressure using machine learning and IoT.EXPERT SYSTEMS WITH APPLICATIONS,209.
MLA	Barriga, Jose A.,et al."Crop-water assessment in Citrus (Citrus sinensis L.) based on continuous measurements of leaf-turgor pressure using machine learning and IoT".EXPERT SYSTEMS WITH APPLICATIONS 209(2022).