Evidence map›Paper›PMID 39639876›Full record

ArticlePlant phenomics (Washington, D.C.)2024

Estimating Leaf Nitrogen Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in Wheat.

Yuanyuan Pan, Jingyu Li, Jiayi Zhang, Jiaoyang He, Zhihao Zhang, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian

Abstract read
In one paragraph

Article in Plant phenomics (Washington, D.C.), 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

10 authors.

Yuanyuan PanNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.ORCID https://orcid.org/0009-0000-9793-6363
Jingyu LiNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Jiayi ZhangNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Jiaoyang HeNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Zhihao ZhangNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Xia YaoNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Tao ChengNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Yan ZhuNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Weixing CaoNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Yongchao TianNational Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.ORCID https://orcid.org/0000-0002-3725-2231

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accuracy of leaf nitrogen accumulation (LNA) estimation is often compromised by the vertical heterogeneity of crop nitrogen. In this study, an estimation model of LNA considering vertical heterogeneity of wheat was developed based on unmanned aerial vehicle (UAV) multispectral data and near-ground hyperspectral data, both collected at different view zenith angles (e.g., 0°, -30°, and -45°). Winter wheat plants were evenly divided into 3 layers from top to bottom, and LNA was obtained for the upper, middle, and lower leaf layers, as well as for various combinations of these layers (upper and middle, middle and lower, and the entire canopy, referred to as LNA

Identifiers

PMID39639876
PMCPMC11617620

What OpenQuestion holds

Textmetadata
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.