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The use of Specim IQ, a hyperspectral camera, for plant analysis

https://doi.org/10.18699/VJ19.587

Abstract

Remote sensing using hyperspectral cameras is an important technology for non-destructive monitoring of plant pigment composition, which is closely related to their physiological state or infection with pathogens. The paper presents the experience of using Specim IQ, a mobile hyperspectral camera, to study common root rot (the pathogen is the fungus Bipolaris sorokiniana Shoem.) affecting the seedlings of four wheat varieties and to analyze the pulp of potato tubers of 82 lines and varieties. Spectral characteristics were obtained for seedlings and the most informative spectral features (indices) for root rot detection were determined based on the data obtained. Seedlings of control variants in the visible part of the spectrum show an increase in reflectance with a small peak in the green area (about 550 nm), then a decrease due to light absorption by plant pigments with an extremum at a wavelength of about 680 nm. Analysis of histograms of vegetation index values demonstrated that the TVI and MCARI indices are the most informative for detecting the pathogen on wheat seedlings according to hyperspectral survey data. For potato samples, regions of the spectrum were found that correspond to local maxima and minima of reflection. It was shown that the spectra of potato varieties have the greatest differences within wavelength ranges of 900-1000 nm and 400-450 nm, which in the former case may be associated with the level of water content, and in the latter, with the formation of melanin in the tubers. It was shown that according to the characteristics of the spectrum, the samples studied are divided into three groups, each characterized by increased or reduced intensity levels for the specified parts of the spectrum. In addition, minima in the reflection spectra corresponding to chlorophyll a were found for a number of varieties. The results demonstrate the capabilities of the Specim IQ camera for conducting hyperspectral analyses of plant objects.

About the Authors

V. V. Alt
Siberian Federal Research Center for Agrobiotechnologies of the Russian Academy of Sciences
Russian Federation

Krasnoobsk, Novosibirsk region



T. A. Gurova
Siberian Federal Research Center for Agrobiotechnologies of the Russian Academy of Sciences
Russian Federation

Krasnoobsk, Novosibirsk region



O. V. Elkin
Siberian Federal Research Center for Agrobiotechnologies of the Russian Academy of Sciences
Russian Federation

Krasnoobsk, Novosibirsk region



D. N. Klimenko
Siberian Federal Research Center for Agrobiotechnologies of the Russian Academy of Sciences
Russian Federation

Krasnoobsk, Novosibirsk region



L. V. Maximov
Institute of Automation and Electrometry, Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



I. A. Pestunov
Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



O. A. Dubrovskaya
Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



M. A. Genaev
Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



T. V. Erst
Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



K. A. Genaev
Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



E. G. Komyshev
Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



V. K. Khlestkin
Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



D. А. Afonnikov
Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University
Russian Federation
Novosibirsk


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