An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture.

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models.

Cellular localization of the Arabidopsis class 2 phytoglobin influences somatic embryogenesis.

Mutation of phytoglobin 2 (Pgb2) increases the number of somatic embryos in Arabidopsis. To assess the effects of the cellular localization of Pgb2 on embryo formation, an inducible system expressing a fusion protein consisting of Pgb2 linked to the steroid-binding domain of the rat glucocorticoid receptor (GR) was introduced in a pgb2 mutant line lacking the ability to express Pgb2. In this transgenic system, Pgb2 remains in the cytoplasm but migrates into the nucleus upon exposure to dexamethasone (DEX).