Version 1
: Received: 21 December 2021 / Approved: 21 December 2021 / Online: 21 December 2021 (12:23:13 CET)
How to cite:
Solovchenko, A.; Shurygin, B.; Kuzin, A.; Velichko, V.; Solovchenko, O.; Nikolenko, A.; Krylov, A. Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping. Preprints2021, 2021120325. https://doi.org/10.20944/preprints202112.0325.v1
Solovchenko, A.; Shurygin, B.; Kuzin, A.; Velichko, V.; Solovchenko, O.; Nikolenko, A.; Krylov, A. Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping. Preprints 2021, 2021120325. https://doi.org/10.20944/preprints202112.0325.v1
Solovchenko, A.; Shurygin, B.; Kuzin, A.; Velichko, V.; Solovchenko, O.; Nikolenko, A.; Krylov, A. Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping. Preprints2021, 2021120325. https://doi.org/10.20944/preprints202112.0325.v1
APA Style
Solovchenko, A., Shurygin, B., Kuzin, A., Velichko, V., Solovchenko, O., Nikolenko, A., & Krylov, A. (2021). Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping. Preprints. https://doi.org/10.20944/preprints202112.0325.v1
Chicago/Turabian Style
Solovchenko, A., Alexandr Nikolenko and Andrey Krylov. 2021 "Enrichment of the Information Extracted From Hyperspectral Reflectance Images for Noninvasive Phenotyping" Preprints. https://doi.org/10.20944/preprints202112.0325.v1
Abstract
Hyperspectral reflectance imaging is an emerging method for rapid non-invasive quantitative screening of plant traits. This method is essential for high-throughput phenotyping and hence for accelerated breeding of crop plants as well as for precision agriculture practices. However, extraction of sensible information from reflectance images is hindered by the complexity of plant optical properties, especially when they are measured in the field. We propose using reflectance indices (Plant Senescence Reflectance Index, PSRI; Anthocyanin Reflectance Index, ARI; and spectral deconvolution) previously developed for remote sensing of vegetation and point-based reflectometers to infer the spatially resolved information on plant development and biochemical composition using ripening apple fruit as the model. Specifically, the proposed approach enables capturing data on distribution of chlorophylls and primary carotenoids as well as secondary carotenoids (both linked with fruit ripening and leaf senescence during plant development) as well as the information on spatial distribution of anthocyanins (known as stress pigments) over the plant surface. We argue that the proposed approach would enrich the phenotype assessments made on the base of reflectance image analysis with valuable information on plant physiological condition, stress acclimation state, and the progression of the plant development.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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