Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I.

ARIMA FAIR datasets artificial neural networks autoregressive integrated moving average convolutional neural networks discrete time series forecasting hive monitoring sensors long short-term memory precision apiculture predictive hive monitoring

Journal

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

Informations de publication

Date de publication:
04 Oct 2024
Historique:
received: 17 08 2024
revised: 29 09 2024
accepted: 29 09 2024
medline: 16 10 2024
pubmed: 16 10 2024
entrez: 16 10 2024
Statut: epublish

Résumé

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (

Identifiants

pubmed: 39409473
pii: s24196433
doi: 10.3390/s24196433
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : United States Department of Agriculture
ID : 2024-67013-42521

Auteurs

Vladimir A Kulyukin (VA)

Department of Computer Science, Utah State University, Logan, UT 84322, USA.

Daniel Coster (D)

Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA.

Aleksey V Kulyukin (AV)

Department of Data Analytics and Information Systems, Huntsman School of Business, Utah State University, Logan, UT 84322, USA.

William Meikle (W)

Carl Hayden Bee Research Center, United States Department of Agriculture, Agricultural Research Service, Tucson, AZ 85719, USA.

Milagra Weiss (M)

Carl Hayden Bee Research Center, United States Department of Agriculture, Agricultural Research Service, Tucson, AZ 85719, USA.

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