Preprint Article Version 1 This version is not peer-reviewed

Morphometry of the Wheat Spike by Analyzing 2D Images

Version 1 : Received: 9 June 2019 / Approved: 12 June 2019 / Online: 12 June 2019 (12:39:18 CEST)

A peer-reviewed article of this Preprint also exists.

Genaev, M.A.; Komyshev, E.G.; Smirnov, N.V.; Kruchinina, Y.V.; Goncharov, N.P.; Afonnikov, D.A. Morphometry of the Wheat Spike by Analyzing 2D Images. Agronomy 2019, 9, 390. Genaev, M.A.; Komyshev, E.G.; Smirnov, N.V.; Kruchinina, Y.V.; Goncharov, N.P.; Afonnikov, D.A. Morphometry of the Wheat Spike by Analyzing 2D Images. Agronomy 2019, 9, 390.

Journal reference: Agronomy 2019, 9, 390
DOI: 10.3390/agronomy9070390

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.

Subject Areas

image analysis; machine learning; algorithms; computer vision

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