Zoom in on the Plant: Fine-Grained Analysis of Leaf, Stem, and Vein Instances

Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive the plant architecture with its individual leaves an...

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Vydáno v:IEEE robotics and automation letters Ročník 9; číslo 2; s. 1588 - 1595
Hlavní autoři: Guldenring, Ronja, Andersen, Rasmus Eckholdt, Nalpantidis, Lazaros
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Agricultural robots
Annotations
Crops
Data models
Datasets
Feature extraction
field robots
Grasslands
Image categorization
image dataset
keypoint-guided polylines
Phenotypes
phenotyping
Plants (botany)
Robotics and automation in agriculture and forestry
Robots
Scene analysis
Stems
ISSN:2377-3766, 2377-3766
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Shrnutí:Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive the plant architecture with its individual leaves and branching system. In this work, we want to advance the granularity in plant understanding for agricultural precision robots. We develop a model to extract fine-grained phenotypic information, such as leaf-, stem-, and vein instances. The underlying dataset RumexLeaves is made publicly available and is the first of its kind with keypoint-guided polyline annotations leading along the line from the lowest stem point along the leaf basal to the leaf apex. Furthermore, we introduce an adapted metric POKS complying with the concept of keypoint-guided polylines. In our experimental evaluation, we provide baseline results for our newly introduced dataset while showcasing the benefits of POKS over OKS.
Bibliografie:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3346807