Evidence map›Paper›PMID 40218711›Full record

ArticleSensors (Basel, Switzerland)2025

Evaluation of Low-Cost Multi-Spectral Sensors for Measuring Chlorophyll Levels Across Diverse Leaf Types.

Prattana Lopin, Pichapob Nawsang, Srisangwan Laywisadkul, Kyle V Lopin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

4 authors.

Prattana LopinDepartment of Biology, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand.ORCID 0009-0007-9021-4799
Pichapob NawsangDepartment of Biology, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand.
Srisangwan LaywisadkulDepartment of Biology, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand.
Kyle V LopinDepartment of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand.ORCID 0000-0001-8635-9572

Funding

Thailand Research Fund RDG622007
6 · The paper itself

Abstract

Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolute error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with validation R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and from 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.

Indexed as

ChlorophyllPlant LeavesSpectrum AnalysisLeast-Squares AnalysisNitrogenChlorophyllNitrogenchlorophyll sensorslearning curveslow-cost sensorsmachine learningmodel validationnested cross-validationpartial least squares regressionspectral reflectancespectral sensors

Identifiers

PMID40218711
PMCPMC11991415

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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.