HopBox: An image analysis pipeline to characterize hop cone morphology Article Swipe
Kayla R. Altendorf
,
Garett C. Heineck
,
Collins Wakholi
,
Anna L. Tawril
,
Pranav Raja
,
Devin A. Rippner
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/ppj2.20080
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/ppj2.20080
Hop cone morphology can influence picking and drying ability, and color can impact consumer preference and may be indicative of quality. However, these characteristics are not generally evaluated in hop breeding programs due to the tedious nature of trait quantification and the extensive variation among cones within a genotype. We developed the HopBox, which is a simply constructed light box with a camera mount, and a publicly available image processing pipeline that identifies hop cones within color‐corrected images, reads a QR code within the image, and outputs data on hop cone length, width, area, perimeter, openness, weight, color, and density. The trained model was applied to images of 500 cones each from 15 replicated advanced hop genotypes from the USDA‐ARS breeding program in Prosser, Washington. Analysis of variance revealed significant ( p < 0.001) differences between genotypes for all traits measured, enabling breeders to discriminate between genotypes for selection purposes. Broad sense heritability for all traits ranged from 0.23 to 0.59. A random sampling of hop cones from the complete dataset revealed that imaging only 5–10 cones adequately captured genotypic variation and provided acceptable rank correlations ( r s > 0.75); however, increasing the sample size to 30 provided optimal precision. Instructions for constructing a HopBox and the code for the analysis pipeline are publicly available online and have wide applicability for hop breeding and research.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ppj2.20080
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ppj2.20080
- OA Status
- gold
- Cited By
- 3
- References
- 37
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4386106661Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/ppj2.20080Digital Object Identifier
- Title
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HopBox: An image analysis pipeline to characterize hop cone morphologyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-22Full publication date if available
- Authors
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Kayla R. Altendorf, Garett C. Heineck, Collins Wakholi, Anna L. Tawril, Pranav Raja, Devin A. RippnerList of authors in order
- Landing page
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https://doi.org/10.1002/ppj2.20080Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ppj2.20080Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ppj2.20080Direct OA link when available
- Concepts
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Heritability, Artificial intelligence, Trait, Computer vision, Hop (telecommunications), Statistics, Computer science, Biology, Mathematics, Genetics, Programming language, Computer networkTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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