Abstract: The present invention discloses a CIELAB color space-based quantitative testing and analysis method for rice seed viability, and the method comprises removing husks on surfaces of rice seeds, soaking and imbibing the rice seeds without the husks removed, staining the rice seeds by using a TTC staining solution, and longitudinally cutting the rice seeds along embryos; converting the longitudinal section image of the rice seed from an RGB color space to a CIELAB color space, and calculating L value, a value and b value per unit area of the rice embryo as a staining intensity per unit area, and converting the staining intensity into a rice seed viability score according to a pre-established conversion model of the staining intensity and the rice seed viability score. According to the present invention, a reference result can be provided for the rapid, accurate and objective evaluation of the seed viability.

Abstract: A unified system with a machine learning feature data pipeline that can be shared among various product areas or teams of an electronic platform is described. A set of features can be fetched from multiple feature sources. The set of features can be combined with browsing event data to generate combined data. The combined data can be sampled to generate sampled data. The sampled data can be presented in a format having a structure that is agnostic to a feature source from which the set of features was fetched. The sampled data can be joined with old features by a backfilling process to generate training data designed to train one or more machine learning models. Related methods, apparatuses, articles of manufacture, and computer program products are also described.