ArticleGigaScience2020
Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Open RGB imaging workflow for morphological and morphometric analysis of fruits using deep learning: a case study on almonds.GigaScience · 2026Article
- Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment.Frontiers in plant science · 2025Article
- Machine Learning-Based Tomato Fruit Shape Classification System.Plants (Basel, Switzerland) · 2024Article
- A novel method for vegetable and fruit classification based on using diffusion maps and machine learning.Current research in food science · 2024Article
- Development of a deep-learning phenotyping tool for analyzing image-based strawberry phenotypes.Frontiers in plant science · 2024Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Using phenomics to identify and integrate traits of interest for better-performing common beans: A validation study on an interspecific hybrid and its Acutifolii parents.Frontiers in plant science · 2022Article
- Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet.Frontiers in plant science · 2022Article
- Genetic and phenotypic analyses reveal major quantitative loci associated to fruit size and shape traits in a non-flat peach collection (P. persica L. Batsch).Horticulture research · 2021Article
- Multivariate Analysis Approaches for Dimension and Shape Discrimination ofPlants (Basel, Switzerland) · 2021Article
- Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.Plant reproduction · 2021Article
- Genomic Informed Breeding Strategies for Strawberry Yield and Fruit Quality Traits.Frontiers in plant science · 2021Article
- Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry.Plant phenomics (Washington, D.C.) · 2021Article
- Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.GigaScience · 2020Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundShape 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.
resultsWe 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.
conclusionsOur 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.
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