BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8
Abstract BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-024-01285-1 |
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| author | Jenyne Loarca Tyr Wiesner-Hanks Hector Lopez-Moreno Andrew F. Maule Michael Liou Maria Alejandra Torres-Meraz Luis Diaz-Garcia Jennifer Johnson-Cicalese Jeffrey Neyhart James Polashock Gina M. Sideli Christopher F. Strock Craig T. Beil Moira J. Sheehan Massimo Iorizzo Amaya Atucha Juan Zalapa |
| author_facet | Jenyne Loarca Tyr Wiesner-Hanks Hector Lopez-Moreno Andrew F. Maule Michael Liou Maria Alejandra Torres-Meraz Luis Diaz-Garcia Jennifer Johnson-Cicalese Jeffrey Neyhart James Polashock Gina M. Sideli Christopher F. Strock Craig T. Beil Moira J. Sheehan Massimo Iorizzo Amaya Atucha Juan Zalapa |
| author_sort | Jenyne Loarca |
| collection | DOAJ |
| description | Abstract BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines. |
| format | Article |
| id | doaj-art-56865f1a509c4bb79553b41173547ac3 |
| institution | Kabale University |
| issn | 1746-4811 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-56865f1a509c4bb79553b41173547ac32025-01-19T12:25:14ZengBMCPlant Methods1746-48112024-11-0120111910.1186/s13007-024-01285-1BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8Jenyne Loarca0Tyr Wiesner-Hanks1Hector Lopez-Moreno2Andrew F. Maule3Michael Liou4Maria Alejandra Torres-Meraz5Luis Diaz-Garcia6Jennifer Johnson-Cicalese7Jeffrey Neyhart8James Polashock9Gina M. Sideli10Christopher F. Strock11Craig T. Beil12Moira J. Sheehan13Massimo Iorizzo14Amaya Atucha15Juan Zalapa16Department of Plant and Agroecosystem Sciences, University of Wisconsin-MadisonCornell University-Breeding InsightDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-MadisonDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-MadisonDepartment of Statistics, University of Wisconsin-MadisonDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-MadisonDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-MadisonPhillip E. Marucci Center for Blueberry and Cranberry Research & ExtensionPhillip E. Marucci Center for Blueberry and Cranberry Research & ExtensionPhillip E. Marucci Center for Blueberry and Cranberry Research & ExtensionPhillip E. Marucci Center for Blueberry and Cranberry Research & ExtensionCornell University-Breeding InsightCornell University-Breeding InsightCornell University-Breeding InsightDepartment of Horticultural Science, North Carolina State UniversityDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-MadisonDepartment of Plant and Agroecosystem Sciences, University of Wisconsin-MadisonAbstract BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berries and extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines.https://doi.org/10.1186/s13007-024-01285-1Computer visionDigital phenotypingImage-based phenotypingImage segmentationPlant breedingPomology |
| spellingShingle | Jenyne Loarca Tyr Wiesner-Hanks Hector Lopez-Moreno Andrew F. Maule Michael Liou Maria Alejandra Torres-Meraz Luis Diaz-Garcia Jennifer Johnson-Cicalese Jeffrey Neyhart James Polashock Gina M. Sideli Christopher F. Strock Craig T. Beil Moira J. Sheehan Massimo Iorizzo Amaya Atucha Juan Zalapa BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8 Plant Methods Computer vision Digital phenotyping Image-based phenotyping Image segmentation Plant breeding Pomology |
| title | BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8 |
| title_full | BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8 |
| title_fullStr | BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8 |
| title_full_unstemmed | BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8 |
| title_short | BerryPortraits: Phenotyping Of Ripening Traits in cranberry (Vaccinium macrocarpon Ait.) with YOLOv8 |
| title_sort | berryportraits phenotyping of ripening traits in cranberry vaccinium macrocarpon ait with yolov8 |
| topic | Computer vision Digital phenotyping Image-based phenotyping Image segmentation Plant breeding Pomology |
| url | https://doi.org/10.1186/s13007-024-01285-1 |
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