Minamikawa Mai F, Nonaka Keisuke, Hamada Hiroko, Shimizu Tokurou, Iwata Hiroyoshi
Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), Shizuoka, Japan.
Front Plant Sci. 2022 Feb 10;13:832749. doi: 10.3389/fpls.2022.832749. eCollection 2022.
"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.
“基因组学辅助育种”利用基于基因组学的方法，例如全基因组关联研究（GWAS）和基因组选择（GS），已经引起了关注，尤其是在水果育种领域。支持基因组辅助育种的低成本基因分型技术已经建立。然而，高效收集大量高质量的表型数据对于这种育种的成功至关重要。大多数水果品质性状已由专业育种人员通过感官和视觉进行评估。然而，作为这种感官和视觉判断基础的水果形态特征尚不清楚。这使得利用图像分析收集关于水果品质性状的有效表型数据变得困难。在本研究中，我们开发了一种通过柑橘类水果横截面图像的图像分析自动测量柑橘类水果形态特征的方法。我们应用可解释机器学习方法和贝叶斯网络来确定水果形态特征与两个感官评估的水果品质性状之间的关系：剥皮难易程度（Peeling）和果实硬度（FruH）。在本研究应用的所有方法中，果实中心核的降解面积均与Peeling和FruH显著且直接相关，而种子面积仅与FruH显著且直接相关。除一两种方法外，白皮层的降解面积和黄皮层的面积也分别与Peeling和FruH显著且直接相关。这些结果表明，一种结合可解释机器学习方法、贝叶斯网络和图像分析的方法在剖析育种人员的经验感知方面可能是有效的。在育种计划中，收集水果图像并有效测量和记录与水果品质性状相关的水果形态特征，可能会增加用于分析和提高柑橘类水果品质性状GWAS和GS准确性的数据量。
