With abundant available genomic data, genomic selection has become routine in many plant breeding programs. Multispectral data captured by UAVs showed potential for grain yield (GY) prediction in many plant species using machine learning; however, the possibilities of utilizing this data to augment genomic prediction models still need to be explored. We collected high-throughput phenotyping (HTP) multispectral data in a genotyped multi-environment large-scale field trial using two cost-effective cameras to fill this gap. We tested back to back the prediction ability of GY prediction models, including genomic (G matrix), multispectral-derived (M matrix), and environmental (E matrix) relationships using best linear unbiased predictor (BLUP) methodology in single and multi-environment scenarios. We discovered that M allows for GY prediction comparable to the G matrix and that models using both G and M matrices show superior accuracies and errors compared with G or M alone, both in single and multi-environment scenarios. We showed that the M matrix is not entirely environment-specific, and the genotypic relationships become more robust with more data capture sessions over the season. We discovered that the optimal time for data capture occurs during grain filling and that camera bands with the highest heritability are important for GY prediction using the M matrix. We showcased that GY prediction can be performed using only an RGB camera, and even a single data capture session can yield valuable data for GY prediction. This study contributes to a better understanding of multispectral data and its relationships. It provides a flexible framework for improving GS protocols without significant investments or software customization.

凭借丰富的可用基因组数据，基因组选择已成为许多植物育种计划的常规方法。无人机捕获的多光谱数据显示出利用机器学习预测许多植物物种的谷物产量 (GY) 的潜力；然而，利用这些数据来增强基因组预测模型的可能性仍然需要探索。我们使用两台经济高效的相机在基因分型多环境大规模现场试验中收集了高通量表型（HTP）多光谱数据来填补这一空白。我们在单环境和多环境中使用最佳线性无偏预测器 (BLUP) 方法背对背测试了 GY 预测模型的预测能力，包括基因组（G 矩阵）、多光谱衍生（M 矩阵）和环境（E 矩阵）关系场景。我们发现，M 允许进行与 G 矩阵相当的 GY 预测，并且在单环境和多环境场景中，与单独使用 G 或 M 相比，使用 G 和 M 矩阵的模型显示出更高的精度和误差。我们表明，M 矩阵并不完全是环境特定的，并且随着季节中更多数据捕获会话的进行，基因型关系变得更加稳健。我们发现数据采集的最佳时间发生在灌浆期间，并且具有最高遗传力的相机波段对于使用 M 矩阵进行 GY 预测非常重要。我们展示了仅使用 RGB 相机即可执行 GY 预测，甚至单个数据捕获会话也可以为 GY 预测产生有价值的数据。这项研究有助于更好地理解多光谱数据及其关系。它提供了一个灵活的框架来改进 GS 协议，无需大量投资或软件定制。