Rating pome fruit quality traits using deep learning and image processing
Abstract Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit‐quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment....
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Plant Direct |
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| Online Access: | https://doi.org/10.1002/pld3.70005 |
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| author | Nhan H. Nguyen Joseph Michaud Rene Mogollon Huiting Zhang Heidi Hargarten Rachel Leisso Carolina A. Torres Loren Honaas Stephen Ficklin |
| author_facet | Nhan H. Nguyen Joseph Michaud Rene Mogollon Huiting Zhang Heidi Hargarten Rachel Leisso Carolina A. Torres Loren Honaas Stephen Ficklin |
| author_sort | Nhan H. Nguyen |
| collection | DOAJ |
| description | Abstract Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit‐quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse‐grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine‐learning and image‐processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long‐established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale ‐omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open‐source and freely available. |
| format | Article |
| id | doaj-art-f0706b17a7a543a9ac778114a0cc51ae |
| institution | Kabale University |
| issn | 2475-4455 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Direct |
| spelling | doaj-art-f0706b17a7a543a9ac778114a0cc51ae2025-02-04T08:31:56ZengWileyPlant Direct2475-44552024-10-01810n/an/a10.1002/pld3.70005Rating pome fruit quality traits using deep learning and image processingNhan H. Nguyen0Joseph Michaud1Rene Mogollon2Huiting Zhang3Heidi Hargarten4Rachel Leisso5Carolina A. Torres6Loren Honaas7Stephen Ficklin8Department of Horticulture Washington State University Pullman WA USAAgricultural Research Service, Physiology and Pathology of Tree Fruits Research Unit ‐ Hood River Worksite USDA Hood River OR USADepartment of Horticulture Washington State University Pullman WA USADepartment of Horticulture Washington State University Pullman WA USAAgricultural Research Service, Physiology and Pathology of Tree Fruits Research Unit USDA Wenatchee WA USAAgricultural Research Service, Physiology and Pathology of Tree Fruits Research Unit ‐ Hood River Worksite USDA Hood River OR USADepartment of Horticulture Washington State University Pullman WA USAAgricultural Research Service, Physiology and Pathology of Tree Fruits Research Unit USDA Wenatchee WA USADepartment of Horticulture Washington State University Pullman WA USAAbstract Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit‐quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse‐grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine‐learning and image‐processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long‐established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale ‐omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open‐source and freely available.https://doi.org/10.1002/pld3.70005machine learningpome fruittrait prediction |
| spellingShingle | Nhan H. Nguyen Joseph Michaud Rene Mogollon Huiting Zhang Heidi Hargarten Rachel Leisso Carolina A. Torres Loren Honaas Stephen Ficklin Rating pome fruit quality traits using deep learning and image processing Plant Direct machine learning pome fruit trait prediction |
| title | Rating pome fruit quality traits using deep learning and image processing |
| title_full | Rating pome fruit quality traits using deep learning and image processing |
| title_fullStr | Rating pome fruit quality traits using deep learning and image processing |
| title_full_unstemmed | Rating pome fruit quality traits using deep learning and image processing |
| title_short | Rating pome fruit quality traits using deep learning and image processing |
| title_sort | rating pome fruit quality traits using deep learning and image processing |
| topic | machine learning pome fruit trait prediction |
| url | https://doi.org/10.1002/pld3.70005 |
| work_keys_str_mv | AT nhanhnguyen ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT josephmichaud ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT renemogollon ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT huitingzhang ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT heidihargarten ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT rachelleisso ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT carolinaatorres ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT lorenhonaas ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing AT stephenficklin ratingpomefruitqualitytraitsusingdeeplearningandimageprocessing |