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Research Article | Open Access

Awn Image Analysis and Phenotyping Using BarbNet

Narendra Narisetti1( ), Muhammad Awais2, Muhammad Khan2, Frieder Stolzenburg3, Nils Stein2,4, Evgeny Gladilin1
Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Molecular Genetics, 06466 Seeland, Germany
Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Genebank, 06466 Seeland, Germany
Harz University of Applied Sciences, Automation and Computer Sciences Department, 38855 Wernigerode, Germany
Center of integrated Breeding Research (CiBreed), Department of Crop Sciences, Georg-August-University, 37075 Göttingen, Germany
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Abstract

Consideration of the properties of awns is important for the phenotypic description of grain crops. Awns have a number of important functions in grasses, including assimilation, mechanical protection, and seed dispersal and burial. An important feature of the awn is the presence or absence of barbs—tiny hook-like single-celled trichomes on the outer awn surface that can be visualized using microscopic imaging. There are, however, no suitable software tools for the automated analysis of these small, semi-transparent structures in a high-throughput manner. Furthermore, automated analysis of barbs using conventional methods of pattern detection and segmentation is hampered by high variability of their optical appearance including size, shape, and surface density. In this work, we present a software tool for automated detection and phenotyping of barbs in microscopic images of awns, which is based on a dedicated deep learning model (BarbNet). Our experimental results show that BarbNet is capable of detecting barb structures in different awn phenotypes with an average accuracy of 90%. Furthermore, we demonstrate that phenotypic traits derived from BarbNet-segmented images enable a quite robust categorization of 4 contrasting awn phenotypes with an accuracy of >85%. Based on the promising results of this work, we see that the proposed model has potential applications in the automation of barley awns sorting for plant developmental analysis.

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Plant Phenomics
Volume 5,
2023
Article number: 0081
DOI: 10.34133/plantphenomics.0081
Cite this article:
Narisetti N, Awais M, Khan M, et al. Awn Image Analysis and Phenotyping Using BarbNet. Plant Phenomics, 2023, 5: 0081. https://doi.org/10.34133/plantphenomics.0081

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Received: 13 April 2023
Accepted: 23 July 2023
Published: 04 August 2023
© 2023 Narendra Narisetti et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).
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