Analysis of Wheat Spike Morphological Traits Using 2D Imaging

Abstract

The morphological structure of wheat spikes plays a central role in wheat yield. Wheat spike morphology, closely associated with crop yield, has attracted considerable attention in the fields of genetics and breeding. However, traditional measurement methods can only measure simple traits, and precise phenotypes remain difficult to obtain, constraining the study and improvement of complex spike-related traits. This study utilized deep learning technologies to develop a pipeline, called SpikePheno, for the acquisition of precise wheat spike phenotypes. Our pipeline demonstrated high accuracy in spike segmentation, achieving a mean Intersection over Union (mIoU) of 0.948. Additionally, our method accurately identified spikelet counts, achieving an R 2 of 0.9923. Using experimental data of 221 wheat cultivars from various regions of China grown in Zhao County, Hebei Province, our pipeline extracted 45 different phenotypes and studied their correlations with thousand grain weight (TGW) and spike yield. Our findings indicate that precise measurement of spike area, spikelet area, and other phenotypic traits enables a clearer understanding of the correlation between spike morphology and wheat yield. Through hierarchical clustering based on spike morphology, we categorized wheat spikes into six classes and identified phenotypic differences between these classes and their impact on TGW and yield. Furthermore, this study revealed phenotypic differences between wheat cultivars from different geographical regions and over different decades, with an increase in large-spike cultivars over time, especially in southern China. This research may help breeders understand the relationship between wheat spike morphology and yield, providing an important basis for future wheat breeding efforts.

Plant phenotyping relevance

SpikePhenoという深層学習画像解析パイプラインを開発し、コムギ穂の分割・小穂数同定・複数形質抽出を中心に評価しているため、植物フェノタイピング手法論文に該当する。

Code and data availability

The paper's SpikePheno phenotyping pipeline (code and trained models) is deposited on GitHub, but the authors explicitly state the repository will only become public after acceptance, so it is not yet publicly actionable. A OneDrive peer-review link exists but its full URL does not match any allowed_urls entry. No phen

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