Extraction of Wheat Spike Phenotypes From Field-Collected Lidar Data and Exploration of Their Relationships With Wheat Yield

Exploring the relationship between spike phenotypes and wheat yield is crucial for selecting wheat ideotypes, but remains a subject of ongoing debate, primarily due to the lack of efficient spike phenotyping methods, particularly in field environments with complex light conditions. Light detection and ranging (lidar) can precisely capture 3-D plant information, minimally affected by light conditions, providing an ideal data source for addressing the abovementioned bottleneck. However, few studies have successfully segmented individual spikes from field-collected lidar data, hindering the extraction of spike phenotypes. Here, we present a novel approach that integrates the kernel-predicting convolution neural network (KP-CNN) with density-based spatial clustering and Laplacian-based region growing techniques for spike segmentation. Our results showed that the proposed approach enabled accurate segmentation of individual spikes, yielding an F-score of 84.62%. Eight spike phenotypes were successfully extracted from individual spike lidar data, including spike density, spike length, spike width, spike curvature, spike inclination angle, spike height, spike area, and spike volume. Notably, the accuracy of spike length and spike width reached levels of 99% and 65%, respectively, with relative root-mean-squared errors (rRMSEs) of 3.99% and 32.03%. All spike phenotypes exhibited significant positive correlations with wheat yield, collectively accounting for 53% of the variations in wheat yield as determined by a random forest (RF) model. The characteristics of spike phenotypes were effective indicators for discerning yield variations among wheat varieties, highlighting spike phenotypes hold significant value in wheat ideotype selection, and lidar has great potential to expedite the field-based wheat breeding cycle.