Scoring Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems.
Journal
Plant phenomics (Washington, D.C.)
ISSN: 2643-6515
Titre abrégé: Plant Phenomics
Pays: United States
ID NLM: 101769942
Informations de publication
Date de publication:
2020
2020
Historique:
received:
06
12
2019
accepted:
30
05
2020
entrez:
14
12
2020
pubmed:
15
12
2020
medline:
15
12
2020
Statut:
epublish
Résumé
Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination. Several variables are extracted from the images (spot density and spot size for UGV, green fraction for UGV and UAV) and related to visual scores assessed by an expert. Results show that spot density and green fraction are critical variables to assess low and high CLS severities, respectively, which emphasizes the importance of having submillimeter images to early detect CLS in field conditions. Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV- and UAV-derived scores. While UGV shows the best estimation performance, UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired. Advantages and limitations of UGV, UAV, and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.
Identifiants
pubmed: 33313567
doi: 10.34133/2020/9452123
pmc: PMC7706347
doi:
Types de publication
Journal Article
Langues
eng
Pagination
9452123Informations de copyright
Copyright © 2020 S. Jay et al.
Déclaration de conflit d'intérêts
The authors declare that there is no conflict of interest regarding the publication of this article.
Références
Sensors (Basel). 2017 Dec 22;18(1):
pubmed: 29271909
Front Plant Sci. 2018 Jul 23;9:1074
pubmed: 30083181
Plant Dis. 1998 Jul;82(7):716-726
pubmed: 30856938
Sensors (Basel). 2017 Jun 18;17(6):
pubmed: 28629159
Plant Sci. 2019 May;282:14-22
pubmed: 31003607
Sci Rep. 2018 Oct 29;8(1):15933
pubmed: 30374139
Plant Dis. 2016 Feb;100(2):241-251
pubmed: 30694129
Sci Total Environ. 2004 Aug 15;329(1-3):29-41
pubmed: 15262156
Front Plant Sci. 2016 Sep 22;7:1419
pubmed: 27713752
Funct Plant Biol. 2016 Feb;44(1):1-9
pubmed: 32480541
Plant Methods. 2018 Jun 8;14:45
pubmed: 29930695
Plant Dis. 2005 Feb;89(2):153-158
pubmed: 30795217
IEEE Trans Neural Netw. 1994;5(6):989-93
pubmed: 18267874