Improved YOLOv8 Model for Phenotype Detection of Horticultural Seedling Growth Based on Digital Cous...
Improved YOLOv8 Model for Phenotype Detection of Horticultural Seedling Growth Based on Digital Cousin
About this item
Full title
Author / Creator
Song, Yuhao , Yang, Lin , Li, Shuo , Yang, Xin , Ma, Chi , Huang, Yuan and Hussain, Aamir
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
Subjects
More information
Scope and Contents
Contents
Crop phenotype detection is a precise way to understand and predict the growth of horticultural seedlings in the smart agriculture era to increase the cost-effectiveness and energy efficiency of agricultural production. Crop phenotype detection requires the consideration of plant stature and agricultural devices, like robots and autonomous vehicles...
Alternative Titles
Full title
Improved YOLOv8 Model for Phenotype Detection of Horticultural Seedling Growth Based on Digital Cousin
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_bafc62737e454d239d79366f0c0fe77a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_bafc62737e454d239d79366f0c0fe77a
Other Identifiers
ISSN
2077-0472
E-ISSN
2077-0472
DOI
10.3390/agriculture15010028
