SeedGerm-VIG: an open and comprehensive pipeline to quantify seed vigour in wheat and other cereal crops using deep learning powered dynamic phenotypic analysis

As one of the most important cereal crops, wheat (Triticum aestivum L.) production and grain quality are essential to many nations in the world. Early developmental phases such as seed germination and seedling establishment are key to wheat’s growth and development as they impact directly on crop’s early performance and yield potential. Hence, it is critical to develop varieties with favourable early growth characteristics under various growing conditions. Here, we present SeedGerm-VIG, an automated and comprehensive pipeline developed for assessing seed vigour in wheat and other cereal crops. Building on the SeedGerm system, we integrated multiple deep learning models (i.e. YOLOv8x-Germ and optimised U-Net) and computer vision algorithms into the automated seed-level analysis pipeline to identify key germination phases and measure seed-, root-, and seedling-level phenotypic traits. Then, by using time series directed graph, not only did we track root tips to measure root emergence during the germination procedure (seed-lot R2 = 84.1%), but we also established a new approach to examine speed and uniformity of germination. These resulted in the establishment of a vigour scoring matrix, through which 21 commercial genotypes’ (i.e. 494 randomly sampled seeds, with over 29,500 seed-level images) vigour scores were summarised and evaluated at key phases such as protrusion, radicle emergence, and chloroplast biogenesis, which largely matched with manual assessment based on the International Seed Testing Association (ISTA) guidelines. Finally, we also demonstrated that the SeedGerm-VIG pipeline could be used to assess seed vigour for other cereal crops such as rice (n = 120 seeds) and barley (n = 240 seeds), reliably. In conclusion, we believe that our work demonstrates a valuable step forward to enable the broader plant and crop research community to examine seed vigour and vigour-related features in an automated manner, facilitating effective and reproducible plant selection and relevant seed science research for crop improvement.