Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

Abstract

Strawberry uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a strong candidate for marker-assisted breeding. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification. This tool has allowed us to illustrate the use of advanced image analysis towards the breeding of greater uniformity in strawberry.

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

“…This hampers using 3D technologies as massively as 2D, although 3D has a number of advantages, mainly a far more realistic and comprehensive fruit representation. For instance, Li et al [ 71 ] utilize 3D imaging to assess fruit uniformity and show that it can be characterized by combining up to six linear parameters.…”

“…Future improvements are still needed as, e.g., image segmentation is not always simple in field conditions and many additional phenotypes are of commercial interest (e.g., uniformity, blemishes). Future improvements should also address additional technological developments such as spectral and MIR images [ 17 ] and 3D imaging [ 71 ]. Finally, a word of caution is that the user should be aware that artificial intelligence tools need thorough training in the specific conditions on which they are going to be employed and that optimizing algorithms may not be that simple.…”

Measurement of Potato Volume With Laser Triangulation and Three-Dimensional Reconstruction

“…However, the analysis of phenotypic features is mostly artificial [6], which is labor-intensive and inefficient, resulting in a so-called "phenotyping bottleneck" [7], [8]. Using computer vision [9] or three-dimensional (3D) imaging [10], farmers can analyze the quality of crops, so they can estimate yields and grades [11].…”