Evidence map›Paper›PMID 37808616›Full record

ArticleJournal of applied statistics2023

Rapid online plant leaf area change detection with high-throughput plant image data.

Yinglun Zhan, Ruizhi Zhang, Yuzhen Zhou, Vincent Stoerger, Jeremy Hiller, Tala Awada, Yufeng Ge

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yinglun ZhanDepartment of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA.
Ruizhi ZhangDepartment of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA.
Yuzhen ZhouDepartment of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA.
Vincent StoergerAgricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, USA.
Jeremy HillerSchool of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA.
Tala AwadaSchool of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA.
Yufeng GeDepartment of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

Indexed as

adaptive cusumADMM algorithmhigh-throughput plant phenotyping (HTPP)online detectionplant leaf areaSupervised learning

Identifiers

PMID37808616
PMCPMC10557544

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.