Rating pome fruit quality traits using deep learning and image processing.

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作者： Nhan H,Nguyen ^[1] ; Joseph,Michaud ^[2] ; Rene,Mogollon ^[3] ; Huiting,Zhang ^[4] ; Heidi,Hargarten ^[5] ; Rachel,Leisso ^[2] ; Carolina A,Torres ^[3] ; Loren,Honaas ^[5] ; Stephen,Ficklin ^[1]

作者单位： Department of Horticulture Washington State University Pullman WA USA. ^[1] Agricultural Research Service, Physiology and Pathology of Tree Fruits Research Unit - Hood River Worksite USDA Hood River OR USA. ^[2] Department of Horticulture Washington State University Pullman WA USA.;Department of Horticulture, Tree Fruit Research and Extension Center Washington State University Wenatchee WA USA. ^[3] Department of Horticulture Washington State University Pullman WA USA.;Agricultural Research Service, Physiology and Pathology of Tree Fruits Research Unit USDA Wenatchee WA USA. ^[4] Agricultural Research Service, Physiology and Pathology of Tree Fruits Research Unit USDA Wenatchee WA USA. ^[5]

关键词 machine learning pome fruit trait prediction

DOI 10.1002/pld3.70005

PMID 39385758

发布时间 2024-10-11

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2024年8卷10期

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Rating pome fruit quality traits using deep learning and image processing.

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Rating pome fruit quality traits using deep learning and image processing.

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