Deep learning object detection to estimate the nectar sugar mass of flowering vegetation

Abstract Floral resources are a key driver of pollinator abundance and diversity, yet their quantification in the field and laboratory is laborious and requires specialist skills. Using a dataset of 25,000 labelled tags of fieldwork‐realistic quality, a convolutional neural network (Faster R‐CNN) wa...

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Bibliografische gegevens
Hoofdauteurs:	Damien Hicks, Mathilde Baude, Christoph Kratz, Pierre Ouvrard, Graham Stone
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Wiley 2021-07-01
Reeks:	Ecological Solutions and Evidence
Onderwerpen:	convolutional neural network deep learning Faster R‐CNN nectar sugar mass object detection pollinator food resources
Online toegang:	https://doi.org/10.1002/2688-8319.12099
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Deep learning object detection to estimate the nectar sugar mass of flowering vegetation

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