Quantifying certain physiological traits under heat-stress is crucial for maximizing genetic gain for wheat yield and yield-related components. In-season estimation of different physiological traits related to heat stress tolerance can ensure the finding of germplasm, which could help in making effective genetic gains in yield. However, estimation of those complex traits is time- and labor-intensive. Unmanned aerial vehicle (UAV) based hyperspectral imaging could be a powerful tool to estimate indirectly in-season genetic variation for different complex physiological traits in plant breeding that could improve genetic gains for different important economic traits, like grain yield. This study aims to predict in-season genetic variations for cellular membrane thermostability (CMT), yield and yield related traits based on spectral data collected from UAVs; particularly, in cases where there is a small sample size to collect data from and a large range of features collected per sample. In these cases, traditional methods of yield-prediction modeling become less robust. To handle this, a functional regression approach was employed that addresses limitations of previous techniques to create a model for predicting CMT, grain yield and other traits in wheat under heat stress environmental conditions and when data availability is constrained. The results preliminarily indicate that the overall models of each trait studied presented a good accuracy compared to their data’s standard deviation. The yield prediction model presented an average error of 13.42%, showing the function-on-function algorithm chosen for the model as reliable for small datasets with high dimensionality.

量化热应激下的某些生理特征对于最大限度地提高小麦产量和产量相关成分的遗传增益至关重要。与热胁迫耐受性相关的不同生理性状的季节性估计可以确保种质的发现，这有助于在产量上获得有效的遗传收益。然而，对这些复杂特征的估计是耗时且费力的。基于无人机 (UAV) 的高光谱成像可以成为间接估计植物育种中不同复杂生理性状的季节性遗传变异的有力工具，可以提高不同重要经济性状（如粮食产量）的遗传收益。本研究旨在预测细胞膜热稳定性 (CMT) 的季节性遗传变异，基于从无人机收集的光谱数据的产量和产量相关性状；尤其是在要从中收集数据的样本量较小且每个样本收集的特征范围较大的情况下。在这些情况下，传统的良率预测建模方法变得不那么稳健。为了解决这个问题，我们采用了一种函数回归方法，该方法解决了先前技术的局限性，以创建一个模型，用于在热应激环境条件下和数据可用性受到限制时预测小麦的 CMT、谷物产量和其他性状。结果初步表明，所研究的每个性状的整体模型与其数据的标准偏差相比具有良好的准确性。产量预测模型的平均误差为 13.42%，