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

Lucas Costa,Jordan Mcbreen,Mostafa Reisi Gahrooei,Md Ali Babar,Yiannis Ampatzidis,Jia Guo

doi:10.1007/s11119-021-09852-5

Lucas Costa, Jordan Mcbreen + Show 4 more

Open Access

https://doi.org/10.1007/s11119-021-09852-5

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Abstract

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.

Highlights

Predicting yield early in wheat (Triticum aestivum) breeding programs can reduce the time it takes to produce a new variety and accelerate crop management practices over the season, like market planning, fertilizer application and harvest procedure preparation (Raun et al, 2001)
This study demonstrates that Unmanned aerial vehicle (UAV)-based hyperspectral imaging is a promising high throughput phenotyping tool for predicting genotypic performance for grain yield and complex physiological traits in wheat in a small set of genotypes under stress conditions
This study illustrates the use of hyperspectral UAV systems to predict yield and certain other traits for facultative wheat genotypes under conditions where data is constrained and data points are limited

Summary

Introduction

Predicting yield early in wheat (Triticum aestivum) breeding programs can reduce the time it takes to produce a new variety and accelerate crop management practices over the season, like market planning, fertilizer application and harvest procedure preparation (Raun et al, 2001). Multiple approaches to grain yield prediction in agriculture have been developed, including those using soil analysis combined with plant indices (Raun et al, 2001; Pantazi et al, 2016), weather conditions and time-series analyses (Bell and Fischer, 1994), or multiple plant indices from canopy reflectance (Hansen et al, 2002) As these approaches rely on a small range of features that define the plant, they require large amounts of data to develop robust models that allow for the indirect selection of superior wheat lines through predictions of certain phenotypic parameters

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