Evidence map›Paper›PMID 34961123›Full record

ArticlePlants (Basel, Switzerland)2021

Development and Implementation of an IoT-Enabled Optimal and Predictive Lighting Control Strategy in Greenhouses.

Shirin Afzali, Sahand Mosharafian, Marc W van Iersel, Javad Mohammadpour Velni

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plants (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Shirin AfzaliSchool of Electrical and Computer Engineering, University of Georgia, 111 Boyd Graduate Studies Research Center, 200 D.W. Brooks Drive, Athens, GA 30602, USA.ORCID 0000-0001-8102-5362
Sahand MosharafianSchool of Electrical and Computer Engineering, University of Georgia, 111 Boyd Graduate Studies Research Center, 200 D.W. Brooks Drive, Athens, GA 30602, USA.ORCID 0000-0003-3958-8043
Marc W van IerselDepartment of Horticulture, University of Georgia, 1111 Miller Plant Sciences Building, Athens, GA 30602, USA.ORCID 0000-0001-5491-0622
Javad Mohammadpour VelniSchool of Electrical and Computer Engineering, University of Georgia, 111 Boyd Graduate Studies Research Center, 200 D.W. Brooks Drive, Athens, GA 30602, USA.ORCID 0000-0001-8546-221X

Funding

United States Department of Agriculture #2018-51181-28365
6 · The paper itself

Abstract

Global population growth has increased food production challenges and pushed agricultural systems to deploy the Internet of Things (IoT) instead of using conventional approaches. Controlling the environmental parameters, including light, in greenhouses increases the crop yield; nonetheless, the electricity cost of supplemental lighting can be high, and hence, the importance of applying cost-effective lighting methods arises. In this research paper, a new optimal supplemental lighting approach was developed and implemented in a research greenhouse by adopting IoT technology. The proposed approach minimizes electricity cost by leveraging a Markov-based sunlight prediction, plant light needs, and a variable electricity price profile. Two experimental studies were conducted inside a greenhouse with "Green Towers" lettuce

Indexed as

image processingInternet of Things (IoT)optimal controlsupplemental lighting in greenhouses

Identifiers

PMID34961123
PMCPMC8703560

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.