Evidence map›Paper›PMID 39253573›Full record

ArticleFrontiers in plant science2024

Design and implementation of a portable snapshot multispectral imaging crop-growth sensor.

Yongxian Wang, Jingwei An, Jianshuang Wu, Mingchao Shao, Jiacheng Wang, Xia Yao, Xiaohu Zhang, Chongya Jiang, Yongchao Tian, Weixing Cao and 2 more

Abstract read
In one paragraph

Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Yongxian WangCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Jingwei AnCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Jianshuang WuCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Mingchao ShaoCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Jiacheng WangCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Xia YaoCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Xiaohu ZhangCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Chongya JiangCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Yongchao TianCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Weixing CaoCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Dong ZhouCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.
Yan ZhuCollege of Agriculture, Nanjing Agricultural University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

Indexed as

crop growth monitoringfield experimentsmosaic filter spectroscopyportable snapshot multispectral imaging crop-growth sensorprediction modelswide band co-optical path imaging system

Identifiers

PMID39253573
PMCPMC11381302

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.