Defining strawberry shape uniformity using 3D imaging and genetic mapping

Abstract

Strawberry shape 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 shape 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 potential candidate for marker-assisted breeding, while the potential to implement genomic selection is explored. 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 shape uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification and unlocked the ability to accurately select for uniform berries.

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

“…The number of seeds is also important economically, but we found that a very high resolution is needed to quantify them. Finally, 3D approaches have also been evaluated in fruits, including strawberry [ 67 , 68 ]. Three-D imaging is far more demanding in terms of sample collection and computationally than 2D [ 69 , 70 ].…”

The Genetic Architecture of Strawberry Yield and Fruit Quality Traits

“…Figure 1). The lack of relationship represents a discord between the desirability of a given shape (as detailed in Li et al 2020 (34)) and the biologically measurable trait H/W. However, H/W or a similar metric, is needed to study the underlying genetic components associated with the trait and thus allow the modification of shape through genome informed breeding.…”

“…Traits were rated for importance in breeding on a scale from 1 (not important) to 9 (highly important) as defined by breeders at NIAB EMR. 3D imaging was conducted as outlined in Li et al, (2020) (34), the height to width ratio (H/W) was calculated using 3D berry images and used to represent strawberry shape. Firmness measures were taken using a FirmTech FT7 machine (UP GmbH, Ibbenbüren, Germany).…”

“…The strawberry phenotyping process from the picking of strawberries through to destructive assessments. Each box represents a discrete phenotyping station *Uniformity of shape and 3D imaging have been reported by Li et al (2020) (34). …”

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

“…For example, uniformity is a key shape factor that is directly tied to fruit quality and sales volume. Li et al [108] defined eight uniformity variables calculated from the 3D architecture of the strawberry fruit and evaluated the importance of each variable for manual uniformity assessment. The results showed that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score.…”