Evidence map›Paper›PMID 36850415›Full record

ReviewSensors (Basel, Switzerland)2023

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects.

Md Sultan Mahmud, Azlan Zahid, Anup Kumar Das

Abstract readReview
In one paragraph

Review 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

3 authors.

Md Sultan MahmudDepartment of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209, USA.ORCID 0000-0002-8141-105X
Azlan ZahidDepartment of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA.ORCID 0000-0001-6202-8680
Anup Kumar DasDepartment of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA.

Funding

United States Department of Agriculture NIFA Accession No. 7003739 and Organizational Project No. TENX2203-CCOCP; NIFA Federal Appropriations under TEX09954 and Accession No. 7002248
6 · The paper itself

Abstract

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production.

Indexed as

Artificial IntelligenceTechnologyAutomationCrop ProductionProspective Studiesagricultural mechanizationartificial intelligencecomputer visiondigital agricultureinternet-of-thingsplant biometricssmart irrigationsmart sprayingstress detection

Identifiers

PMID36850415
PMCPMC9966776

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.