Evidence map›Paper›PMID 37426960›Full record

ArticleFrontiers in plant science2023

Dynamic monitoring of maize grain quality based on remote sensing data.

Weiwei Sun, Qijin He, Jiahong Liu, Xiao Xiao, Yaxin Wu, Sijia Zhou, Selimai Ma, Rongwan Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2023. 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

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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

8 authors.

Weiwei SunCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Qijin HeCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Jiahong LiuCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Xiao XiaoCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Yaxin WuCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Sijia ZhouCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Selimai MaCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Rongwan WangCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Remote sensing data have been widely used to monitor crop development, grain yield, and quality, while precise monitoring of quality traits, especially grain starch and oil contents considering meteorological elements, still needs to be improved. In this study, the field experiment with different sowing time, i.e., 8 June, 18 June, 28 June, and 8 July, was conducted in 2018-2020. The scalable annual and inter-annual quality prediction model for summer maize in different growth periods was established using hierarchical linear modeling (HLM), which combined hyperspectral and meteorological data. Compared with the multiple linear regression (MLR) using vegetation indices (VIs), the prediction accuracy of HLM was obviously improved with the highest

Indexed as

grain qualitymaize (Zea mays L.)modelquality monitoringspectral remote sensing

Identifiers

PMID37426960
PMCPMC10325687

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.