Scanning the rice Global MAGIC population for dynamic genetic control of seed traits under vegetative drought

Abstract Grain size and shape are important yield components in rice (Oryza sativa L.), and their genetic control under adverse environmental conditions, such as drought stress, remains uncertain. This lack of knowledge is due to the laborious, time‐consuming phenotyping of seed attributes in large rice populations. We developed a new high‐throughput phenotyping method based on a desktop scanner and the open‐source package Plant Computer Vision (PlantCV). We used this method to investigate seed size and shape variability within the rice Global multi‐parent advanced generation intercross population, grown under well‐watered and vegetative drought conditions. Besides being affordable, rapid, and accurate, our method captured the phenotypic divergence between drought and well‐watered samples, expressed as 12 different traits, including new grain shape metrics. Our method facilitates the identification of outperforming genotypes under drought stress so is potentially very valuable for crop breeders seeking to make selection based on kernel attributes. We ran a marker–trait association analysis for the measured seed traits, demonstrating that our method also provides adequate phenotypes for genetic studies. We observed dynamic genetic control of seed‐related traits under vegetative drought stress, highlighting the importance of understanding the contribution of genotype × environment interactions on trait variation to develop resilient, high‐yielding rice cultivars