Evidence map›Paper›PMID 32352533›Full record

ArticleGigaScience2020

Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.

Mitchell J Feldmann, Michael A Hardigan, Randi A Famula, Cindy M López, Amy Tabb, Glenn S Cole, Steven J Knapp

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mitchell J FeldmannDepartment of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.
Michael A HardiganDepartment of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.
Randi A FamulaDepartment of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.
Cindy M LópezDepartment of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.
Amy TabbUSDA-ARS-AFRS, 2217 Wiltshire Rd, Kearneysville, WV 25430, USA.
Glenn S ColeDepartment of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.
Steven J KnappDepartment of Plant Sciences, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Genetic Association StudiesMachine LearningPhenotypeAlgorithmsFragariaFruitInheritance PatternsModels, TheoreticalFragaria × ananassafruit shapelatent space phenotypesmachine learningmorphometricsprincipal progression of k clusters

Identifiers

PMID32352533
PMCPMC7191992

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.