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DOI	10.1016/j.compag.2021.106311
	Robotics-based vineyard water potential monitoring at high resolution
	Saiz-Rubio, Veronica; Rovira-Mas, Francisco; Cuenca-Cuenca, Andres; Alves, Fernando
通讯作者	Rovira-Mas, F (corresponding author), Univ Politecn Valencia, Agr Robot Lab ARL, Camino Vera S-N, Valencia 46022, Spain.
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2021
卷号	187
英文摘要	The purpose of this research is deploying a proximal sensing solution using non-invasive and cost-effective sensors onboard an Autonomous Ground Vehicle (AGV) as a feasible way for building high-resolution maps of water potential in vineyards. The final objective is offering growers a practical system to make decisions about water management, especially for arid climatic conditions. The monitoring AGV was entirely developed within this research context, and as a result, it is a machine specifically designed to endure off-road conditions and harsh environments. The autonomous vehicle served as a massive, non-invasive, and on-the-go data collector robotic platform. The sensors used for measuring the relevant field variables were two spectral reflectance sensors (SRS), an infrared radiometer, and an on-board weather sensor. The collected data were displayed on comprehensible grid maps using the Local Tangent Plane (LTP) coordinate system. The proposed model has a coefficient of determination R-2 of 0.69, and results from combining six parameters: the canopy and air temperatures (as the temperature difference), the relative humidity, the altitude difference, the Normalized Difference Vegetation Index (NDVI), and the Photochemical Reflectance Index (PRI). The strongest relationships found in this study were between the temperature difference and PRI, with an R-2 of 0.75, and the temperature difference with the leaf water potential with an R-2 of 0.61. The practical use of these high-resolution maps includes irrigation scheduling and harvest zoning for sorting grape quality, with a further use as inputs to complex artificial intelligence algorithms considering larger areas or complementing airborne data. Future improvements to make the models more robust and versatile will entail considering additional variables, locations, or grapevine cultivars, and even other crops grown in vertical trellis systems.
英文关键词	Precision Agriculture PRI Plant water potential Proximal sensing Autonomous Ground Vehicle (AGV)
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000696733500001
WOS关键词	PHOTOCHEMICAL REFLECTANCE INDEX ; ASSESSING CANOPY PRI ; STRESS DETECTION ; CLIMATE-CHANGE ; LEAF ; TEMPERATURE ; INDICATOR ; IMAGERY
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368538
作者单位	[Saiz-Rubio, Veronica; Rovira-Mas, Francisco; Cuenca-Cuenca, Andres] Univ Politecn Valencia, Agr Robot Lab ARL, Camino Vera S-N, Valencia 46022, Spain; [Alves, Fernando] Vinhos SA, Symington Family Estates, Travessa Barao de Forrester 86, P-4431901 Vila Nova De Gaia, Portugal
推荐引用方式 GB/T 7714	Saiz-Rubio, Veronica,Rovira-Mas, Francisco,Cuenca-Cuenca, Andres,et al. Robotics-based vineyard water potential monitoring at high resolution[J],2021,187.
APA	Saiz-Rubio, Veronica,Rovira-Mas, Francisco,Cuenca-Cuenca, Andres,&Alves, Fernando.(2021).Robotics-based vineyard water potential monitoring at high resolution.COMPUTERS AND ELECTRONICS IN AGRICULTURE,187.
MLA	Saiz-Rubio, Veronica,et al."Robotics-based vineyard water potential monitoring at high resolution".COMPUTERS AND ELECTRONICS IN AGRICULTURE 187(2021).