Evidence map›Paper›PMID 35722263›Full record

ArticlePeerJ2022

Droplet distribution in cotton canopy using single-rotor and four-rotor unmanned aerial vehicles.

Yanhua Meng, Yan Ma, Zhiguo Wang, Hongyan Hu

Abstract read
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Yanhua MengSchool of Mechanical Engineering, Anyang Institute of Technology, Anyang, Henan Province, China.
Yan MaInstitute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan Province, China.
Zhiguo WangKey Laboratory of Aviation Plant Protection, Ministry of Agriculture and Rural Affairs, Anyang, Henan Province, China.
Hongyan HuInstitute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, Henan Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unmanned aerial vehicles (UAVs) are widely used as the sprayers for low-volume pesticide application in recent years. Droplet distribution characteristics of UAV spraying in the cotton canopy have notable effect on the biological control efficacy of the targets and the defoliation efficiency of the harvest aids. In this work, the influences on droplet distribution in the cotton canopy with respect to the flight height, forward mode, and spraying volume were evaluated by conducting the field trials during two cotton growth stages in 2020, respectively. The first field trial was performed in the cotton flowering stage and the second one was conducted in the early boll development stage. Two typical UAVs equipped with a single-rotor and four-rotor, respectively, were adopted as the spraying platforms in this work. Droplet deposition obtained by water sensitive papers (WSPs) clipped on the cotton leaves was considered as the observing metric. All cotton leaves in the canopy were divided into three groups (

Indexed as

PesticidesUnmanned Aerial DevicesGossypiumPlant LeavesPesticidesCotton canopyDroplet distributionForward modeSpraying parametersUAV

Identifiers

PMID35722263
PMCPMC9205310

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.