Evidence map›Paper›PMID 40391034›Full record

ArticleFrontiers in plant science2025

Image analysis using smartphones: relationship between leaf color and fresh weight of lettuce under different nutritional treatments.

Hye Su Won, Eunji Lee, Seeun Lee, Ji-Hyeon Nam, Jiwon Jung, Yuna Cho, Thomas Evert, Noah Kan, Steven Kim, Dong Sub Kim

Abstract read
In one paragraph

Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Hye Su Won *Department of Horticulture, Kongju National University, Yesan, Republic of Korea.
Eunji Lee *Department of Horticulture, Kongju National University, Yesan, Republic of Korea.
Seeun Lee *Department of Biological Science, Kongju National University, Gongju, Republic of Korea.
Ji-Hyeon NamDepartment of Horticulture, Kongju National University, Yesan, Republic of Korea.
Jiwon JungDepartment of Horticulture, Kongju National University, Yesan, Republic of Korea.
Yuna ChoDepartment of Horticulture, Kongju National University, Yesan, Republic of Korea.
Thomas EvertDepartment of Mathematics and Statistics, California State University, Monterey Bay, Seaside, CA, United States.
Noah KanDepartment of Mathematics and Statistics, California State University, Monterey Bay, Seaside, CA, United States.
Steven KimDepartment of Mathematics and Statistics, California State University, Monterey Bay, Seaside, CA, United States.
Dong Sub KimDepartment of Horticulture, Kongju National University, Yesan, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Image analysis can be useful for assessing crop health and predicting yield. Instead of expensive equipment, smartphones are considered an accessible and low-cost alternative. The objectives of this study were to evaluate whether fresh weight in green and red lettuce could be predicted by leaf color (intensity of green color measured by RGB) under different fertilizer treatments using RGB imaging from two widely used smartphone models (Samsung Galaxy and Apple iPhone). The two smartphones showed similar longitudinal patterns of RGB data (the intensity and dark green proportion), but the absolute difference in the RGB data was significantly different. Therefore, the averaged results were used for the analyses. Color intensity and dark green proportion were associated with the fresh lettuce weight (p = 0.005, 0.003, 0.014 and p < 0.001, respectively). This study suggests that farmers and practitioners can use these economic devices as a non-destructive method to diagnose and monitor the nutritional status and predict lettuce yield.

Indexed as

Bland-Altman analysisdark green proportiongreen and red lettucesnormalized intensityRGB

Identifiers

PMID40391034
PMCPMC12086155

What OpenQuestion holds

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LicenceCC BY
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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.