Application of Machine Learning Techniques to Discern Optimal Rearing Conditions for Improved Black Soldier Fly Farming.

Internet of Things black soldier fly insect for feed insects farming machine learning

Journal

Insects
ISSN: 2075-4450
Titre abrégé: Insects
Pays: Switzerland
ID NLM: 101574235

Informations de publication

Date de publication:
19 May 2023
Historique:
received: 20 03 2023
revised: 15 05 2023
accepted: 16 05 2023
medline: 26 5 2023
pubmed: 26 5 2023
entrez: 26 5 2023
Statut: epublish

Résumé

As the world population continues to grow, there is a need to come up with alternative sources of feed and food to combat the existing challenge of food insecurity across the globe. The use of insects, particularly the black soldier fly (BSF)

Identifiants

pubmed: 37233107
pii: insects14050479
doi: 10.3390/insects14050479
pmc: PMC10231057
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Foreign and Commonwealth Office
ID : IMC-Grant 21108

Références

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Auteurs

John Muinde (J)

International Centre of Insect Physiology and Ecology, Nairobi 30772-00100, Kenya.

Chrysantus M Tanga (CM)

International Centre of Insect Physiology and Ecology, Nairobi 30772-00100, Kenya.

John Olukuru (J)

LabAfrica Research Centre, Strathmore University, Nairobi 59857-00200, Kenya.

Clifford Odhiambo (C)

Sanergy Limited, Atlanta, GA 550288, USA.

Henri E Z Tonnang (HEZ)

International Centre of Insect Physiology and Ecology, Nairobi 30772-00100, Kenya.

Kennedy Senagi (K)

International Centre of Insect Physiology and Ecology, Nairobi 30772-00100, Kenya.

Classifications MeSH