Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.
Convolutional Neural Network
biomass yield
data augmentation
phenotyping
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
26 Aug 2020
26 Aug 2020
Historique:
received:
02
07
2020
revised:
04
08
2020
accepted:
12
08
2020
entrez:
30
8
2020
pubmed:
30
8
2020
medline:
26
3
2021
Statut:
epublish
Résumé
Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species
Identifiants
pubmed: 32858803
pii: s20174802
doi: 10.3390/s20174802
pmc: PMC7506807
pii:
doi:
Types de publication
Letter
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul
ID : 59/300.075/2015
Organisme : Conselho Nacional de Desenvolvimento Científico e Tecnológico
ID : 433783/2018-4; 303559/2019-5
Références
Sensors (Basel). 2013 Aug 06;13(8):10027-51
pubmed: 23925082
Nature. 2015 May 28;521(7553):436-44
pubmed: 26017442