Journal Article

FZJ-2024-01194

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Editorial: Computer vision in plant phenotyping and agriculture

Stavness, I. (Corresponding author) ; Giuffrida, V. ; Scharr, H.FZJ*

2023
Frontiers Media Lausanne

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Please use a persistent id in citations: doi:10.3389/frai.2023.1187301  doi:10.34734/FZJ-2024-01194

Abstract: Plant phenotyping is the process of identifying a plant’s structural and functional characteristics. Plant phenotyping is used by plant scientists to uncover mechanisms of plant physiology, eg, to characterize how plants respond to biotic and abiotic stress. Phenotyping is also used by plant breeders to evaluate cultivars in a plant population for beneficial characteristics in order to inform the selection of progeny to move forward within a multiyear breeding process. In an attempt to reduce the time and cost required to phenotype large plant populations, image-based phenotyping has become popular over the past 10 years. Extracting phenotypic information from images of plants and crops presents a number of challenging real-world computer vision problems, such as analyzing images with highly self-similar repeating patterns and analyzing densely packed and occluded plant organs. This Research Topic is associated with the 7th Computer Vision in Plant Phenotyping and Agriculture (CVPPA) workshop, which was held at the International Conference on Computer Vision (ICCV) on 11 October 2021. During the workshop, 18 full-length papers and 14 extended abstracts were presented. This Research Topic includes three papers that are extended versions of abstracts presented at the workshop. The Research Topic also includes 11 new articles that fall under the general scope of CVPPA but were not previously presented at the workshop.The papers in this Research Topic explore a number of high-priority challenges in image-based phenotyping, including curating new datasets, developing few-and zero-shot analysis approaches that do not require …

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2023

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Web of Science Core Collection

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