Narisetti Narendra, Neumann Kerstin, Röder Marion S, Gladilin Evgeny
Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
Front Plant Sci. 2020 Jun 23;11:666. doi: 10.3389/fpls.2020.00666. eCollection 2020.
Spike is one of the crop yield organs in wheat plants. Determination of the phenological stages, including heading time point (HTP), and area of spike from non-invasive phenotyping images provides the necessary information for the inference of growth-related traits. The algorithm previously developed by Qiongyan et al. for spike detection in 2-D images turns out to be less accurate when applied to the European cultivars that produce many more leaves. Therefore, we here present an improved and extended method where (i) wavelet amplitude is used as an input to the Laws texture energy-based neural network instead of original grayscale images and (ii) non-spike structures (e.g., leaves) are subsequently suppressed by combining the result of the neural network prediction with a Frangi-filtered image. Using this two-step approach, a 98.6% overall accuracy of neural network segmentation based on direct comparison with ground-truth data could be achieved. Moreover, the comparative error rate in spike HTP detection and growth correlation among the ground truth, the algorithm developed by Qiongyan et al., and the proposed algorithm are discussed in this paper. The proposed algorithm was also capable of significantly reducing the error rate of the HTP detection by 75% and improving the accuracy of spike area estimation by 50% in comparison with the Qionagyan et al. method. With these algorithmic improvements, HTP detection on a diverse set of 369 plants was performed in a high-throughput manner. This analysis demonstrated that the HTP of 104 plants (comprises of 57 genotypes) with lower biomass and tillering range (e.g., earlier-heading types) were correctly determined. However, fine-tuning or extension of the developed method is required for high biomass plants where spike emerges within green bushes. In conclusion, our proposed method allows significantly more reliable results for HTP detection and spike growth analysis to be achieved in application to European cultivars with earlier-heading types.
穗是小麦植株的产量器官之一。从小麦非侵入性表型图像中确定包括抽穗时间点(HTP)在内的物候阶段以及穗面积,为推断生长相关性状提供了必要信息。琼燕等人先前开发的用于二维图像中穗检测的算法,在应用于叶片更多的欧洲品种时,准确性较低。因此,我们在此提出一种改进和扩展的方法,其中:(i)将小波幅度用作基于Laws纹理能量的神经网络的输入,而不是原始灰度图像;(ii)随后通过将神经网络预测结果与Frangi滤波图像相结合来抑制非穗结构(例如叶片)。使用这种两步法,基于与地面真值数据的直接比较,神经网络分割的总体准确率可达98.6%。此外,本文还讨论了地面真值、琼燕等人开发的算法以及所提出算法之间在穗HTP检测中的比较误差率和生长相关性。与琼燕等人的方法相比,所提出的算法还能够将HTP检测的误差率显著降低75%,并将穗面积估计的准确率提高50%。通过这些算法改进,以高通量方式对369株不同植株进行了HTP检测。该分析表明,正确确定了104株(包括57个基因型)生物量和分蘖范围较低(例如抽穗较早类型)植株的HTP。然而,对于穗在绿色灌木丛中出现的高生物量植株,需要对所开发的方法进行微调或扩展。总之,我们提出的方法在应用于抽穗较早类型的欧洲品种时,能够在HTP检测和穗生长分析中获得显著更可靠的结果。