Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream.

ByteTrack YOLOv5 fruit detection object counting object detection

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
13 Oct 2023
Historique:
received: 01 09 2023
revised: 28 09 2023
accepted: 03 10 2023
medline: 30 10 2023
pubmed: 28 10 2023
entrez: 28 10 2023
Statut: epublish

Résumé

Dragon fruit (

Identifiants

pubmed: 37896537
pii: s23208444
doi: 10.3390/s23208444
pmc: PMC10611008
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : 2023 Guangxi university young and middle-aged teachers scientific research basic ability improvement project, Guangxi Science and Technology Base and Talent Project,Science and Technology Major Project of Guangxi, China
ID : (2023KY0012), (2022AC21272),Gui Ke AA22117004

Références

Front Plant Sci. 2020 Jun 16;11:898
pubmed: 32612632
Sensors (Basel). 2021 Dec 23;22(1):
pubmed: 35009602

Auteurs

Xiuhua Li (X)

School of Electrical Engineering, Guangxi University, Nanning 530004, China.
Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China.

Xiang Wang (X)

School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Pauline Ong (P)

Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia.

Zeren Yi (Z)

School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Lu Ding (L)

School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Chao Han (C)

School of Electrical Engineering, Guangxi University, Nanning 530004, China.

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