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Dimension fitting of wheat spikes in dense 3D point clouds based on the adaptive k-means algorithm with dynamic perspectives

Wang, Fuli and Mohan, Vishwanathan and Thompson, Andrew and Dudley, Richard (2020) Dimension fitting of wheat spikes in dense 3D point clouds based on the adaptive k-means algorithm with dynamic perspectives. In: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 2020-11-04 - 2020-11-06, Trento, Italy.

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Abstract

The use of dense 3D point clouds to obtain agricultural crop dimensions in the place of manual measurement is crucial for enabling high-throughput phenotyping. To achieve this goal, this paper proposes an adaptive k-means algorithm based on dynamic perspectives, which first performs segmentation in order to separate the wheat spikes. We also propose a method to fit the shape of each spike and measures the dimensions of each spike with the help of the Random Sample Consensus algorithm. The experimental results show that the proposed method can be applied in a complex environment where multiple wheat spikes are grown densely and that it can fit the size of most wheat spikes accurately.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 17 Jun 2021 11:17
Last Modified: 17 Jun 2021 12:15
URI: http://repository.essex.ac.uk/id/eprint/30541

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