Phenotyping for heat stress tolerance in wheat population using physiological traits, multispectral imagery, and machine learning approaches

作者：Neelesh Sharma, Manu Kumar, Hans D. Daetwyler, Richard Trethowan, Matthew Hayden, Surya Kant · 发表于：Plant Stress · 年份：2024 · DOI：10.1016/j.stress.2024.100593 · 被引用次数：11 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Spectroscopy and Chemometric Analyses

• Efficient phenotyping methods to evaluate wheat population under field conditions are needed to breed for high-yielding and heat-tolerant wheat varieties. • Here, we phenotyped the response of 184 genotypes of wheat to heat stress using aerial imagery and physiological dataset. • Wheat genotypes were classified using vegetative and stress indices for heat stress tolerance. • The multispectral-driven phenotypic traits can be used by breeders to select and develop heat-tolerant wheat varieties tolerant to heat stress. Heat stress is a critical environmental factor that adversely affects crop productivity. With the increasing frequency and intensity of heat waves and extreme weather events, heat stress has become a challenge for wheat production, which is one of the most important cereal crops. To sustain wheat production under heat stress conditions, there is an urgent need to develop high-yielding, heat-tolerant wheat varieties. This requires characterizing the genetic and physiological mechanisms underlying heat tolerance, as well as developing efficient phenotyping methods to evaluate a large number of wheat genotypes under heat stress field conditions. In this study, we used 184 wheat genotypes that were sown at two times of sowing (TOS), i.e., optimal sowing as TOS1 and late sowing as TOS2, with higher temperatures faced by plants during heading and grain filling in TOS2. We used a combination of physiological traits, multispectral vegetative indices (VIs) derived from a...