Evidence map›Paper›PMID 37050812›Full record

SynthesisSensors (Basel, Switzerland)2023

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review.

Hasyiya Karimah Adli, Muhammad Akmal Remli, Khairul Nizar Syazwan Wan Salihin Wong, Nor Alina Ismail, Alfonso González-Briones, Juan Manuel Corchado, Mohd Saberi Mohamad

Abstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

7 authors.

Hasyiya Karimah AdliFaculty of Data Science & Computing, University Malaysia Kelantan, City Campus, Kota Bharu 16100, Kelantan, Malaysia.ORCID 0000-0001-8754-6206
Muhammad Akmal RemliInstitute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Kota Bharu 16100, Kelantan, Malaysia.
Khairul Nizar Syazwan Wan Salihin WongInstitute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Kota Bharu 16100, Kelantan, Malaysia.
Nor Alina IsmailFaculty of Data Science & Computing, University Malaysia Kelantan, City Campus, Kota Bharu 16100, Kelantan, Malaysia.ORCID 0000-0003-3919-3367
Alfonso González-BrionesGrupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Calle Espejo 2, 37007 Salamanca, Spain.ORCID 0000-0002-3444-4393
Juan Manuel CorchadoGrupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Calle Espejo 2, 37007 Salamanca, Spain.ORCID 0000-0002-2829-1829
Mohd Saberi MohamadHealth Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain 17666, United Arab Emirates.ORCID 0000-0002-1079-4559

Funding

Universiti Malaysia Kelantan R/FUND/A0100/01862A/001/2020/00819Universiti Malaysia Kelantan R/NICE/B0500/00950B/001/2021/00916
6 · The paper itself

Abstract

As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

Indexed as

AgricultureArtificial IntelligenceDatabases, FactualImage Processing, Computer-AssistedTechnologyartificial intelligenceartificial intelligence of thingscrop diseasesinternet of thingspost-pandemicsmart agriculture

Identifiers

PMID37050812
PMCPMC10098529

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.