Asian rice is cultivated worldwide and represents a staple food for half of the global population. With the enormous necessity of increasing rice production while reducing input consumption, identifying high yielding genetic variants under restricted water availability is a priority in rice breeding. Here we investigate the phenotypic variation and genetic inheritance of seed shape and size within a Multi-parent Advanced Generation Inter-Cross (MAGIC) population grown under drought and well-watered conditions. Our evaluated population includes 257 lines developed by inter-crossing 16 parents of elite varieties from the indica and japonica subspecies. Seed shape and size affect rice yield in many direct and indirect ways, are responsive to environmental conditions, and also play a large role in consumer preference. We developed a digital method to measure seed-related traits based on a desktop scanner (Epson Perfection V600 Photo Scanner) and Plant Computer Vision (PlantCV) v3.0, an open-source image processing toolkit for plant imaging analysis. After scanning all seeds per genotype in a simple phenotyping set-up consisting of color standards, a ruler, and a black frame, our PlantCV workflow identifies each seed and measures 14 seed phenotypes (e.g., width, height, perimeter, area), while processing all individual images in parallel. Besides the advantage of acquiring different seed phenotype parameters simultaneously and automatically for one to many seeds per genotype, our method is cost-effective and flexible to any operating system, a benefit which was missing in previous imaging systems developed in rice. By integrating the phenotypes we acquire with previously generated genotyping-by-sequencing (GBS) data, we will uncover the genetic control of seed phenotypes under drought stress in rice, providing a new foundation for the genetic improvement of yield and grain quality traits in Asian rice.
.jpg "Annarita Marrano, PhD photo")