Narisetti Narendra, Awais Muhammad, Khan Muhammad, Stolzenburg Frieder, Stein Nils, Gladilin Evgeny
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.
Plant Phenomics. 2023 Aug 4;5:0081. doi: 10.34133/plantphenomics.0081. eCollection 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.
考虑芒的特性对于谷类作物的表型描述很重要。芒在禾本科植物中具有许多重要功能，包括同化作用、机械保护以及种子传播和掩埋。芒的一个重要特征是芒的外表面是否存在倒刺——微小的钩状单细胞毛状体，可通过显微镜成像观察到。然而，目前还没有合适的软件工具能够以高通量方式对这些小的、半透明的结构进行自动分析。此外，使用传统的模式检测和分割方法对倒刺进行自动分析时，由于其光学外观（包括大小、形状和表面密度）的高度变异性而受到阻碍。在这项工作中，我们展示了一种用于在芒的显微图像中自动检测倒刺并进行表型分析的软件工具，该工具基于一个专用的深度学习模型（BarbNet）。我们的实验结果表明，BarbNet能够以90%的平均准确率检测不同芒表型中的倒刺结构。此外，我们证明从BarbNet分割图像中得出的表型特征能够以>85%的准确率对4种对比鲜明的芒表型进行相当稳健的分类。基于这项工作的良好结果，我们认为所提出的模型在用于植物发育分析的大麦芒分选自动化方面具有潜在应用。
