Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes

• Published in: Precision agriculture Vol. 23; no. 2; pp. 622 - 642
• Main Authors: Costa, Lucas, McBreen, Jordan, Ampatzidis, Yiannis, Guo, Jia, Gahrooei, Mostafa Reisi, Babar, Md Ali
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Atmospheric Sciences; Biomedical and Life Sciences; Cell membranes; Chemistry and Earth Sciences; Computer Science; Crop yield; Data collection; Environmental conditions; Genetic diversity; Genotypes; Germplasm; Heat; Heat stress; Heat tolerance; Hyperspectral imaging; Life Sciences; Physics; Physiology; Plant breeding; Prediction models; Remote Sensing/Photogrammetry; Robustness (mathematics); Seasons; Soil Science & Conservation; Statistics for Engineering; Thermal stability; Unmanned aerial vehicles; Wheat; Function-on-function regression; UAV; Cellular membrane thermostability; Wheat; Grain yield; Machine learning
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-021-09852-5

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.