Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110260
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Type: Journal article
Title: Detecting spikes of wheat plants using neural networks with Laws texture energy
Author: Qiongyan, L.
Cai, J.
Berger, B.
Okamoto, M.
Miklavcic, S.
Citation: Plant Methods, 2017; 13(1):83-1-83-13
Publisher: BioMed Central
Issue Date: 2017
ISSN: 1746-4811
1746-4811
Statement of
Responsibility: 
Li Qiongyan, Jinhai Cai, Bettina Berger, Mamoru Okamoto and Stanley J. Miklavcic
Abstract: Background: The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential. Results: We have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The spike detection step was further improved by removing noise using an area and height threshold. The evaluation results showed an accuracy of over 80% in identification of spikes. In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The correlation between the final average grain yield and spike area is also discussed in this paper. Conclusions: Our highly accurate yield trait phenotyping method for spike number counting and spike area estimation, is useful and reliable not only for grain yield estimation but also for detecting and quantifying subtle phenotypic variations arising from genetic or environmental differences.
Rights: © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
RMID: 0030076854
DOI: 10.1186/s13007-017-0231-1
Grant ID: http://purl.org/au-research/grants/arc/LP150100055
Appears in Collections:Agriculture, Food and Wine publications

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