2023
DOI: 10.1002/aps3.11548
|View full text |Cite
|
Sign up to set email alerts
|

From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2

William N. Weaver,
Stephen A. Smith

Abstract: PremiseQuantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.MethodsLeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 spe… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
7
1

Relationship

2
6

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 66 publications
(102 reference statements)
0
1
0
Order By: Relevance
“…Concerning the models' results for each herb and the model for the classes, there is an opportunity for improvement in the model training process for all aromatic herbs [17,18,35]. A model that does not produce false negatives, i.e., a model in which no undetected bounding boxes should be detected, has a recall of 1.0.…”
Section: Resultsmentioning
confidence: 99%
“…Concerning the models' results for each herb and the model for the classes, there is an opportunity for improvement in the model training process for all aromatic herbs [17,18,35]. A model that does not produce false negatives, i.e., a model in which no undetected bounding boxes should be detected, has a recall of 1.0.…”
Section: Resultsmentioning
confidence: 99%
“…One area where this is proving highly effective is in automatic trait extraction from digitised herbarium specimens (Walker et al, 2022), with pilot studies have shown promising results on different types of plants. For example, in LeafMachine (Weaver et al, 2020;Weaver and Smith, 2023), a CNN algorithm was trained to measure leaf area and perimeter from lowquality images, with a success rate of 60%. In another study, a different CNN algorithm was shown to be capable of discriminating between growth shoots and vegetative structures in tropical plants from French Guiana (Goëau et al, 2022).…”
Section: Example 1b: Label Extraction Within Digitisation Pipelinesmentioning
confidence: 99%
“…At the same time, non-destructive specimen measurements of trace elements can be obtained using X-ray fluorescence, looking for trace metal hyperaccumulation. Interestingly, these multispectral analyses can be combined with tools based on artificial intelligence (AI) technology (Figure 2) enabling computerautomated (or semiautomated) measurement for large-scale analyses of phenological traits in thousands of specimen images [19,22,23].…”
Section: From Figurative Herbaria To Phenological Studiesmentioning
confidence: 99%