AIseed: An automated image analysis software for high-throughput phenotyping and quality non-destructive testing of individual plant seeds.

• The software can extract 54 phenotypic features of various-sized plant seeds. • These features provide efficient separation of seeds with different qualities. • Using features for modeling can realize seed clarity, vigor, and purity testing. • The established models can be applied in AIseed for visual seed quality testing. Seed quality is the key to seedling development, which affects field yield. Researchers, breeders, and processors may need to investigate seed quality on a large scale, which requires high-throughput methods, which are currently lacking. To address this bottleneck, we have developed a novel software named AIseed that automatically captures and analyzes great traits, such as the shape, color, or texture features of individual seeds, which are rarely effortlessly and stably determined by conventional measurements. The AIseed software is based on machine vision technology and enables high-throughput handling and phenotyping of various-sized plant seeds. After phenotyping for a total of 54 features, AIseed numbers each seed to facilitate further seed quality assessment or grain detection. In addition, AIseed includes modules for seed quality detection and prediction, such as seed clarity, purity, vigor, and viability testing, which are based on machine learning or deep learning models developed by analyzing the associations between seed quality indicators and the features obtained by the software. Through a series of experiments, this paper has confirmed that AIseed has a high performance in extracting phenotypic features and testing seed quality from images for seeds of several plants of different sizes, with high speed and high precision in separating seeds from the background and then analyzing them. In summary, it is a non-destructive and highly effective software for accurate seed phenotyping and seed quality assessment, which is essential for both breeding and yield improvement. [ABSTRACT FROM AUTHOR]