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An unsupervised automatic measurement of wheat spike dimensions in dense 3D point clouds for field application

Wang, Fuli and Li, Fengping and Mohan, Vishwanathan and Dudley, Richard and Gu, Dongbing and Bryant, Ruth (2022) 'An unsupervised automatic measurement of wheat spike dimensions in dense 3D point clouds for field application.' Biosystems Engineering, 223. pp. 103-114. ISSN 0021-8634

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Abstract

An accurate measurement of field-grown wheat traits, including spike number, dimension and volume are essential for crop phenotyping and yield analysis. A high-throughput method to image field-grown wheat in three dimensions is presented with an accompanying unsupervised measuring method to obtain individual wheat spike data. Images are captured using four structured light scanners on a field mobile platform, creating dimensionally accurate sub-millimetre resolution 3D point clouds for a 4.5 m3 volume in less than 10 s. The unsupervised method analyses each trial plot's 3D point cloud, containing hundreds of wheat spikes, calculating the average size of the wheat spike and total spike volume per plot. The analysis utilises an adaptive k-means algorithm with dynamic perspectives, to fit each spike's shape and measures the dimensions with a random sample consensus algorithm. The method generates small cuboids to fit all the wheat spikes and estimate the total spikes volume. Experimental results show that the proposed algorithm is a reliable tool for identifying spikes from wheat crops and identifying individual spikes. Compared with the manual measurement, according to the results of five scenes, the average error rate in the number of spikes, spikes' height and spikes' width in tests were 16.27%, 5.24% and 12.38% respectively.

Item Type: Article
Uncontrolled Keywords: k-means; Point clouds; Shape-fitting; Unsupervised algorithm; Wheat phenotype
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 12 Dec 2021 15:52
Last Modified: 29 Oct 2022 22:46
URI: http://repository.essex.ac.uk/id/eprint/31882

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