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Morphometry of the wheat spike by analyzing 2D images. / Genaev, Mikhail A.; Komyshev, Evgenii G.; Smirnov, Nikolai V. et al.

In: Agronomy, Vol. 9, No. 7, 390, 17.07.2019.

Research output: Contribution to journal › Article › peer-review

Harvard

Genaev, MA, Komyshev, EG, Smirnov, NV, Kruchinina, YV, Goncharov, NP & Afonnikov, DA 2019, 'Morphometry of the wheat spike by analyzing 2D images', Agronomy, vol. 9, no. 7, 390. https://doi.org/10.3390/agronomy9070390

APA

Genaev, M. A., Komyshev, E. G., Smirnov, N. V., Kruchinina, Y. V., Goncharov, N. P., & Afonnikov, D. A. (2019). Morphometry of the wheat spike by analyzing 2D images. Agronomy, 9(7), [390]. https://doi.org/10.3390/agronomy9070390

Vancouver

Genaev MA, Komyshev EG, Smirnov NV, Kruchinina YV, Goncharov NP, Afonnikov DA. Morphometry of the wheat spike by analyzing 2D images. Agronomy. 2019 Jul 17;9(7):390. doi: 10.3390/agronomy9070390

Author

Genaev, Mikhail A. ; Komyshev, Evgenii G. ; Smirnov, Nikolai V. et al. / Morphometry of the wheat spike by analyzing 2D images. In: Agronomy. 2019 ; Vol. 9, No. 7.

BibTeX

@article{d4eb6f573ed54e84af35498c5e9bcd35,
title = "Morphometry of the wheat spike by analyzing 2D images",
abstract = "Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.",
keywords = "Algorithms, Computer vision, Image analysis, Machine learning, algorithms, SHAPE, TRITICUM L., computer vision, machine learning, image analysis",
author = "Genaev, {Mikhail A.} and Komyshev, {Evgenii G.} and Smirnov, {Nikolai V.} and Kruchinina, {Yuliya V.} and Goncharov, {Nikolay P.} and Afonnikov, {Dmitry A.}",
note = "Publisher Copyright: {\textcopyright} 2019 by the authors.",
year = "2019",
month = jul,
day = "17",
doi = "10.3390/agronomy9070390",
language = "English",
volume = "9",
journal = "Agronomy",
issn = "2073-4395",
publisher = "MDPI AG",
number = "7",

}

RIS

TY - JOUR

T1 - Morphometry of the wheat spike by analyzing 2D images

AU - Genaev, Mikhail A.

AU - Komyshev, Evgenii G.

AU - Smirnov, Nikolai V.

AU - Kruchinina, Yuliya V.

AU - Goncharov, Nikolay P.

AU - Afonnikov, Dmitry A.

N1 - Publisher Copyright: © 2019 by the authors.

PY - 2019/7/17

Y1 - 2019/7/17

N2 - Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.

AB - Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.

KW - Algorithms

KW - Computer vision

KW - Image analysis

KW - Machine learning

KW - algorithms

KW - SHAPE

KW - TRITICUM L.

KW - computer vision

KW - machine learning

KW - image analysis

UR - http://www.scopus.com/inward/record.url?scp=85070187134&partnerID=8YFLogxK

U2 - 10.3390/agronomy9070390

DO - 10.3390/agronomy9070390

M3 - Article

AN - SCOPUS:85070187134

VL - 9

JO - Agronomy

JF - Agronomy

SN - 2073-4395

IS - 7

M1 - 390

ER -

ID: 21255902