Abstract
“Genomics-assisted breeding”, which utilizes genomics-based methods, e.g., genome-wide association study (GWAS) and genomic selection (GS), has been attracting attention, especially in the field of fruit breeding. Low-cost genotyping technologies that support genome-assisted breeding have already been established. However, efficient collection of large amounts of high-quality phenotypic data is essential for the success of such breeding. Most of the fruit quality traits have been sensorily and visually evaluated by professional breeders. However, the fruit morphological features that serve as the basis for such sensory and visual judgments are unclear. This makes it difficult to collect efficient phenotypic data on fruit quality traits using image analysis. In this study, we developed a method to automatically measure the morphological features of citrus fruits by the image analysis of cross-sectional images of citrus fruits. We applied explainable machine learning methods and Bayesian networks to determine the relationship between fruit morphological features and two sensorily evaluated fruit quality traits: easiness of peeling (Peeling) and fruit hardness (FruH). In each of all the methods applied in this study, the degradation area of the central core of the fruit was significantly and directly associated with both Peeling and FruH, while the seed area was significantly and directly related to FruH alone. The degradation area of albedo and the area of flavedo were also significantly and directly related to Peeling and FruH, respectively, except in one or two methods. These results suggest that an approach that combines explainable machine learning methods, Bayesian networks, and image analysis can be effective in dissecting the experienced sense of a breeder. In breeding programs, collecting fruit images and efficiently measuring and documenting fruit morphological features that are related to fruit quality traits may increase the size of data for the analysis and improvement of the accuracy of GWAS and GS on the quality traits of the citrus fruits.
Highlights
The global demand for high-quality fruits is increasing rapidly, and fruit quality has become an essential breeding target (Jenks and Bebeli, 2011)
Seven [Flesh Lab (a), DegCenter area, Circularity, Seed area, Flavedo area, Locule angle, and Whole area; in the increasing order of regression coefficient] and eight [Flesh area, Flavedo Lab (a), DegCenter area, Whole area, Seed area, Locule number, Locule angle, and Flavedo area; in the increasing order of regression coefficient] fruit morphological features were significantly associated with Peeling and fruit hardness (FruH), respectively, in the multiple linear regression (MLR) (Figure 3A and Supplementary Table 4)
We developed a method to quantitatively and automatically evaluate fruit morphological features by using the image analysis of cross-sectional fruit images of citrus fruits
Summary
The global demand for high-quality fruits is increasing rapidly, and fruit quality has become an essential breeding target (Jenks and Bebeli, 2011). Cross-breeding to obtain cultivars with highquality fruits generally takes many years due to the long juvenile period of fruit trees. In light of this constraint, it is sensible for the breeders to evaluate as many genotypes as possible to increase the acquisition rate of the new varieties. The large size of the fruit trees, makes this to be difficult due to limited orchard space To overcome these barriers of conventional fruit tree breeding, “genomics-assisted breeding,” which utilizes genomic-based methods, such as genome-wide association study (GWAS) and genomic selection (GS), has been attracting attention, especially in the field of fruit breeding (Iwata et al, 2016). The immense potential of GWAS and GS using real breeding populations has already been reported in fruit trees, e.g., citrus (Minamikawa et al, 2017; Imai et al, 2019), apple (Muranty et al, 2015; Minamikawa et al, 2021), and Japanese pear (Minamikawa et al, 2018; Nishio et al, 2021)
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