摘要Wheat spike morphology plays a critical role in determining grain yield and has garnered significant interest in genetics and breeding research.However,traditional measurement methods are limited to simple traits and fail to capture complex spike phenotypes with high precision,thus limiting progress in yield-related trait analysis.In this study,a deep learning pipeline,called Speakerphone,for acquiring precise wheat spike phenotypes was developed.Our pipeline achieved a mean intersection over union(mIoU)of 0.948 in spike segmentation.Additionally,the spike traits measured by our method strongly agreed with the manually measured values,with Pearson correlation coefficients of 0.9865 for spike length,0.9753 for the number of spikelets per spike,and 0.9635 for fertile spikelets.Using experimental data of 221 wheat cultivars from various regions of Zhao County,Hebei Province,China,our pipeline extracted 45 phenotypes and analyzed their correlations with thousand-grain weight(TGW)and spike yield.Our findings indicate that precise measurements of spike area,spikelet area,and other phenotypic traits clarify the correlation between spike morphology and wheat yield.Through hierarchical clustering on the basis of spike morphology,we categorized wheat spikes into six classes and identified the phenotypic differences among these classes and their effects on TGW and yield.Furthermore,phenotypic dif-ferences among wheat cultivars from different geographical regions and over decades were revealed in this study,with an increase in the number of large-spike cultivars over time,especially in southern China.This research may help breeders understand the relationship between wheat spike morphology and yield,thus providing an important basis for future wheat breeding efforts.