Multiparametric Regressions and Machine Learning: UAV Imagery for Temperature Prediction of Irrigated Forage Cactus in the Brazilian Semiarid.

Eric Gabriel Fernandez Albuquerque da Silva, Thayná Alice Brito Almeida, Raví Emanoel de Melo, João Luis Mendes Pedroso de Lima, José Amilton Santos Júnior, Abelardo Antônio de Assunção Montenegro

Abstract read

PubMed Full text at PMC Publisher

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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

–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

0 citing papers in PubMed.

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

6 authors.

Eric Gabriel Fernandez Albuquerque da SilvaDepartment of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, Pernambuco, Brazil.

Thayná Alice Brito AlmeidaPostgraduate Program in Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, Pernambuco, Brazil.ORCID 0000-0002-2172-4732

Raví Emanoel de MeloDepartment of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, Pernambuco, Brazil.

João Luis Mendes Pedroso de LimaMARE-Marine and Environmental Sciences Centre, ARNET-Aquatic Research Network, Department of Civil Engineering, Faculty of Sciences and Technology, University of Coimbra, Rua Luís Reis Santos, Pólo II, 3030-788 Coimbra, Portugal.ORCID 0000-0002-0135-2249

José Amilton Santos JúniorDepartment of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, Pernambuco, Brazil.ORCID 0000-0002-1656-7103

Abelardo Antônio de Assunção MontenegroDepartment of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, Pernambuco, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vegetation surface temperature is a key indicator of crop water status, particularly in semi-arid regions where water availability constrains agricultural production. This study evaluated whether spectral bands and vegetation indices derived from UAV multispectral imagery can predict the surface temperature of irrigated forage cactus (

Indexed as

CactaceaeMachine LearningOpuntiaRemote Sensing TechnologyUnmanned Aerial DevicesAgricultural IrrigationBoosting Machine Learning AlgorithmsBrazilRandom ForestRegression AnalysisTemperatureartificial intelligencecrop water statusprecision agriculturevegetation indices

Identifiers

PMID42884527

DOI10.3390/s26196327

PMCPMC13667663

What OpenQuestion holds

Textmetadata

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.