Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.

ABSTRACT:

Background

Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit shape is an inherently multi-dimensional, continuously variable trait and not adequately described by a single categorical or quantitative feature. Morphometric approaches enable the study of complex, multi-dimensional forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC). We use these human-rec

SUBMITTER: Feldmann MJ 

PROVIDER: S-EPMC7191992 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature