Evidence map›Paper›PMID 37077312›Full record

ArticlePeerJ2023

Detecting and distinguishing between apicultural plants using UAV multispectral imaging.

Alexandros Papachristoforou, Maria Prodromou, Diofantos Hadjimitsis, Michalakis Christoforou

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 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
2.0field-weighted citation impact, top 15% of its field
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

0 citing papers in PubMed, 8 citations in OpenAlex.

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

4 authors at 2 institutions in 2 countries.

Alexandros Papachristoforou *Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Maria Prodromou *Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus.
Diofantos HadjimitsisDepartment of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus.
Michalakis ChristoforouDepartment of Environment and Climate, Eratosthenes Center of Excelence, Limassol, Cyprus.
Cyprus University of Technology · CYAristotle University of Thessaloniki · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting and distinguishing apicultural plants are important elements of the evaluation and quantification of potential honey production worldwide. Today, remote sensing can provide accurate plant distribution maps using rapid and efficient techniques. In the present study, a five-band multispectral unmanned aerial vehicle (UAV) was used in an established beekeeping area on Lemnos Island, Greece, for the collection of high-resolution images from three areas where

Indexed as

BeekeepingPlantsRemote Sensing TechnologyUnmanned Aerial DevicesAnimalsBeesGreecePlant DispersalReproducibility of ResultsApicultureGoogle Earth EngineMultispectral imagingPlant detectionSarcopoterium spinosumThymus capitatusUAV

Identifiers

PMID37077312
PMCPMC10108856
OpenAlexW4365514416

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.