Evidence map›Paper›PMID 32294964›Full record

ArticleSensors (Basel, Switzerland)2020

LeafScope: A Portable High-Resolution Multispectral Imager for In Vivo Imaging Soybean Leaf.

Liangju Wang, Yunhong Duan, Libo Zhang, Jialei Wang, Yikai Li, Jian Jin

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Liangju WangDepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.ORCID 0000-0002-6936-6145
Yunhong DuanDepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.
Libo ZhangDepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.
Jialei WangDepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.
Yikai LiSchool of Industrial Engineering, Purdue University, 315 Grant St., West Lafayette, IN 47907, USA.
Jian JinDepartment of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.
Purdue University West Lafayette · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Portable devices for measuring plant physiological features with their isolated measuring chamber are playing an increasingly important role in plant phenotyping. However, currently available commercial devices of this type, such as soil plant analysis development (SPAD) meter and spectrometer, are dot meters that only measure a small region of the leaf, which does not perfectly represent the highly varied leaf surface. This study developed a portable and high-resolution multispectral imager (named LeafScope) to in-vivo image a whole leaf of dicotyledon plants while blocking the ambient light. The hardware system is comprised of a monochrome camera, an imaging chamber, a lightbox with different bands of light-emitting diodes (LEDs) array, and a microcontroller. During measuring, the device presses the leaf to lay it flat in the imaging chamber and acquires multiple images while alternating the LED bands within seconds in a certain order. The results of an experiment with soybean plants clearly showed the effect of nitrogen and water treatments as well as the genotype differences by the color and morphological features from image processing. We conclude that the low cost and easy to use LeafScope can provide promising imaging quality for dicotyledon plants, so it has great potential to be used in plant phenotyping.

Indexed as

ColorGenotypeGlycine maxImage Processing, Computer-AssistedLinear ModelsPlant Leaveshandheld sensorhigh-resolutionleaf morphological featuresleaf venationmultispectral imagingplant phenotypingsoybean leaf

Identifiers

PMID32294964
PMCPMC7218849
OpenAlexW3016072257

What OpenQuestion holds

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LicenceCC BY
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.