Traits Expansion and Storage of Soybean Phenotypic Data in Computer Vision-Based Test - Xing, Yongchao; Lv, Peixin; He, Hong; Leng, Jiantian; Yu, Hui; Feng, Xianzhong (Frontiers in Plant Science, 2022)
Phenotypic traits of crops are important basis for cultivate new crop varieties. Breeding experts expect to use artificial intelligence (AI) technology and obtain many accurate phenotypic data at a lower cost for the design of breeding programs. Computer vision (CV) has a higher resolution than human vision and has the potential to achieve large-scale, low-cost and accurate analysis and identification of crop phenotypes. the existing criteria for investigating phenotypic traits are oriented to artificial species examination, among which there are few traits types, which cannot meet the needs of machine learning even if the data are complete. Therefore, the research starts from the needs to collect phenotypic data based on CV technology to expand respectively the four types of traits in the "Guide to Plant Variety Specificity, Consistency and Stability Testing: Soybean": main agronomic traits in field investigation, main agronomic traits in indoor survey, resistance traits, and soybean seed phenotypic traits. This paper expounds the role of the new-added phenotypic traits, and show the necessity of adding it with some instances. The expanded traits are important additions and improvement to the existing criteria. Databases containing expanded traits are important sources of data for Soybean AI Breeding Platforms. They are necessary to provide convenience for deep learning and support the experts to design accurate breeding programs.