Evidence map›Paper›PMID 38839822›Full record

ArticleScientific data2024

A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species.

Diogo Castilho, Danilo Tedesco, Carlos Hernandez, Beata Emoke Madari, Ignacio Ciampitti

Abstract readDataset
In one paragraph

Article in Scientific data, 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

5 authors.

Diogo CastilhoGraduate Program in Agronomy, Federal University of Goiás, Goiânia, Goiás, Brazil. diogocastilho6@hotmail.com.ORCID 0000-0003-4721-5351
Danilo TedescoDepartment of Agronomy, Kansas State University, 1712 Claflin Rd., Manhattan, KS, 66506, USA.
Carlos HernandezDepartment of Agronomy, Kansas State University, 1712 Claflin Rd., Manhattan, KS, 66506, USA.
Beata Emoke MadariGraduate Program in Agronomy, Federal University of Goiás, Goiânia, Goiás, Brazil.
Ignacio CiampittiDepartment of Agronomy, Kansas State University, 1712 Claflin Rd., Manhattan, KS, 66506, USA. ciampitti@ksu.edu.ORCID 0000-0001-9619-5129

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enhancing rapid phenotyping for key plant traits, such as biomass and nitrogen content, is critical for effectively monitoring crop growth and maximizing yield. Studies have explored the relationship between vegetation indices (VIs) and plant traits using drone imagery. However, there is a gap in the literature regarding data availability, accessible datasets. Based on this context, we conducted a systematic review to retrieve relevant data worldwide on the state of the art in drone-based plant trait assessment. The final dataset consists of 41 peer-reviewed papers with 11,189 observations for 11 major crop species distributed across 13 countries. It focuses on the association of plant traits with VIs at different growth/phenological stages. This dataset provides foundational knowledge on the key VIs to focus for phenotyping key plant traits. In addition, future updates to this dataset may include new open datasets. Our goal is to continually update this dataset, encourage collaboration and data inclusion, and thereby facilitate a more rapid advance of phenotyping for critical plant traits to increase yield gains over time.

Indexed as

Crops, AgriculturalNitrogenBiomassPhenotypePlantsNitrogen

Identifiers

PMID38839822
PMCPMC11153509

What OpenQuestion holds

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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.