Version 1
: Received: 6 March 2019 / Approved: 7 March 2019 / Online: 7 March 2019 (12:06:15 CET)
How to cite:
Genaev, M.; Komyshev, E.; Smirnov, N.; Kruchinina, Y.; Goncharov, N.P.; Afonnikov, D. Morphometry of the Wheat Spike by Analyzing 2D Images. Preprints2019, 2019030091. https://doi.org/10.20944/preprints201903.0091.v1
Genaev, M.; Komyshev, E.; Smirnov, N.; Kruchinina, Y.; Goncharov, N.P.; Afonnikov, D. Morphometry of the Wheat Spike by Analyzing 2D Images. Preprints 2019, 2019030091. https://doi.org/10.20944/preprints201903.0091.v1
Genaev, M.; Komyshev, E.; Smirnov, N.; Kruchinina, Y.; Goncharov, N.P.; Afonnikov, D. Morphometry of the Wheat Spike by Analyzing 2D Images. Preprints2019, 2019030091. https://doi.org/10.20944/preprints201903.0091.v1
APA Style
Genaev, M., Komyshev, E., Smirnov, N., Kruchinina, Y., Goncharov, N.P., & Afonnikov, D. (2019). Morphometry of the Wheat Spike by Analyzing 2D Images. Preprints. https://doi.org/10.20944/preprints201903.0091.v1
Chicago/Turabian Style
Genaev, M., Nikolay P. Goncharov and Dmitry Afonnikov. 2019 "Morphometry of the Wheat Spike by Analyzing 2D Images" Preprints. https://doi.org/10.20944/preprints201903.0091.v1
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 harvesting and analysis for numerous plants, which is automatable 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.). Models of sections, 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. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections).
The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.
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.
