Vineyard Water Status Estimation with near Infrared Spectroscopy and Data Mining

Leaf water potential is a measure commonly used to describe crop water status and water stress dynamics. The established method for determining leaf water potential using a pressure chamber is cumbersome and subject to operator error as well as time/temperature limitations. These limitations prohibit the intensive sampling required to support proactive water management of commercial crops, including vineyards. Particular for grapevines there is need for faster, more precise and more reliable tools for determining leaf water potential in the field. Portable Near-infrared spectroscopy and multivariate data analysis were applied for the modeling and prediction of leaf water potential in grapevines. For field-grown wine grapes the most significant and intensive leaf absorptions occurs in the region from 1440 to 1950 nm and again beyond 2,200 nm. Multivariate analysis of these spectra, referenced against pressure chamber measurements as a standard, showed correlation coefficients from 0.87 to 0.95 clearly demonstrated that this technology can provide a fast and reasonable assessment of leaf water potential in the field.

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Near Infrared Spectroscopy and Data Mining: Classification of Grapevine Varieties

NIR news ◽

10.1255/nirn.1618 ◽

2016 ◽

Vol 27 (5) ◽

pp. 8-10

Author(s):

Salvador Gutiérrez ◽

Javier Tardáguila ◽

Juan Fernández-Novales ◽

María P. Diago

Keyword(s):

Data Mining ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared

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Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement

Journal of the Science of Food and Agriculture ◽

10.1002/jsfa.8676 ◽

2017 ◽

Vol 98 (5) ◽

pp. 1935-1943 ◽

Cited By ~ 8

Author(s):

Valentina Giovenzana ◽

Roberto Beghi ◽

Simone Parisi ◽

Lucio Brancadoro ◽

Riccardo Guidetti

Keyword(s):

Infrared Spectroscopy ◽

Real Time ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Water Status ◽

Leaf Water ◽

Wavelength Selection ◽

Leaf Water Status ◽

Potential Effectiveness ◽

Selection For

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Near-infrared hyperspectral image at a glance: Some personal thoughts

NIR news ◽

10.1177/0960336020943987 ◽

2020 ◽

Vol 31 (5-6) ◽

pp. 8-14

Author(s):

José Manuel Amigo

Keyword(s):

Machine Learning ◽

Data Mining ◽

Infrared Spectroscopy ◽

Hyperspectral Imaging ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Constructive Criticism ◽

Constructive Discussion

First of all, I want to transmit my most humble thanks to all people who believe that I deserve the “2019 Thomas Hirschfeld” award (kindly supported by FOSS) for my work on near-infrared spectroscopy and, especially, applied on hyperspectral images. I must confess that this award caught me by surprise and that I felt a bit overwhelmed when I received it. It is an honour full of respect and responsibility. I have been given the opportunity of writing this article, and I will profit it to express different personal thoughts about general but relevant aspects of near infrared applied to hyperspectral imaging. Also, since I am more a practitioner in chemometrics (or machine learning or data mining, or …) than a developer, I will also include some insights about the beautiful combination of near-infrared hyperspectral image with chemometrics. This article is just a glimpse of constructive criticism with personal thoughts that comes from my little experience in this field. Therefore, and of course, all opinions here are open for constructive discussion with the only purpose of learning (like the machines do nowadays).

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