1Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Molecular Genetics, 06466 Seeland, Germany
2Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Genebank, 06466 Seeland, Germany
3Harz University of Applied Sciences, Automation and Computer Sciences Department, 38855 Wernigerode, Germany
4Center of integrated Breeding Research (CiBreed), Department of Crop Sciences, Georg-August-University, 37075 Göttingen, Germany
| Received 13 Apr 2023 |
Accepted 23 Jul 2023 |
Published 04 Aug 2023 |
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.
