Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry
Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry
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DOI：
10.1093/gigascience/giaa030
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发表时间：
2020-05-01
期刊：
影响因子：
9.2
通讯作者：
Knapp, Steven J.
中科院分区：
文献类型：
--
作者：
Feldmann, Mitchell J.;Hardigan, Michael A.;Knapp, Steven J.
背景：形状是草莓果实视觉吸引力的关键因素，受遗传和非遗传因素的影响。目前草莓外部特征的水果表型分析方法通常依赖于人眼进行分类评估。然而，果形是一个内在的多维，连续可变的性状，并没有充分描述了一个单一的分类或数量特征。形态测量方法使复杂的，多维的形式的研究，但往往是抽象的，难以解释。在这项研究中，我们开发了一种数学方法，用于将水果形状分类从数字图像转换到称为k聚类主进程（PPKC）的有序尺度上。我们使用这些人类可识别的形状类别来选择从多个形态测量分析中提取的最适合遗传解剖和分析的定量特征。结果如下：我们使用无监督机器学习将草莓水果的图像转换为人类可识别的类别，发现了4个主要形状类别，并使用PPKC推断进展。我们从草莓的数字图像中提取了68个定量特征，使用一套形态分析和多元统计方法。这些分析定义了信息功能集，有效地捕捉形状类之间的定量差异。新创建的表型变量用于描述形状的分类准确率范围为68%至99%。结论：我们的研究结果表明，草莓果实形状可以鲁棒地量化，准确地分类，并使用图像分析，机器学习和PPKC经验排序。我们产生了一个字典的数量性状的研究和预测形状类，并确定遗传因素的草莓果实形状的表型变异。我们在草莓上应用的方法和途径应该适用于其他水果、蔬菜和特色作物。
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-recognizable shape categories to select quantitative features extracted from multiple morphometric analyses that are best fit for genetic dissection and analysis. Results: We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered 4 principal shape categories, and inferred progression using PPKC. We extracted 68 quantitative features from digital images of strawberries using a suite of morphometric analyses and multivariate statistical approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68% to 99% for the newly created phenotypic variables for describing a shape. Conclusions: Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches that we applied in strawberry should apply to other fruits, vegetables, and specialty crops.