Evidence map›Paper›PMID 25202639›Full record

ArticleApplications in plant sciences2014

Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement of leaf area.

Hsien Ming Easlon, Arnold J Bloom

Abstract read
In one paragraph

Article in Applications in plant sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
134citing papers in PubMed, 1 pooled it
–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

134 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  7. SNc Nuclease GenesInternational journal of molecular sciences · 2026
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74 more citing papers are in PubMed but not listed here.

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

2 authors.

Hsien Ming EaslonDepartment of Plant Sciences, University of California, One Shields Avenue, Davis, California 95616 USA.
Arnold J BloomDepartment of Plant Sciences, University of California, One Shields Avenue, Davis, California 95616 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledPREMISE OF THE STUDY: Measurement of leaf areas from digital photographs has traditionally required significant user input unless backgrounds are carefully masked. Easy Leaf Area was developed to batch process hundreds of Arabidopsis rosette images in minutes, removing background artifacts and saving results to a spreadsheet-ready CSV file. • METHODS AND

resultsEasy Leaf Area uses the color ratios of each pixel to distinguish leaves and calibration areas from their background and compares leaf pixel counts to a red calibration area to eliminate the need for camera distance calculations or manual ruler scale measurement that other software methods typically require. Leaf areas estimated by this software from images taken with a camera phone were more accurate than ImageJ estimates from flatbed scanner images. •

conclusionsEasy Leaf Area provides an easy-to-use method for rapid measurement of leaf area and nondestructive estimation of canopy area from digital images.

Indexed as

Arabidopsisdigital imagesleaf areaPython

Identifiers

PMID25202639
PMCPMC4103476

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.