Evidence map›Paper›PMID 35242842›Full record

ArticleFrontiers in veterinary science2022

Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights.

Mark F Hansen, Alphonsus Oparaeke, Ryan Gallagher, Amir Karimi, Fahim Tariq, Melvyn L Smith

Open access · goldAbstract read
In one paragraph

Article in Frontiers in veterinary science, 2022. 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
4.2field-weighted citation impact, top 7% 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

4 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

6 authors at 3 institutions in 2 countries.

Mark F HansenThe Centre for Machine Vision, Bristol Robotics Laboratory, UWE Bristol, Bristol, United Kingdom.
Alphonsus OparaekeDepartment of Crop Science, University of Calabar, Calabar, Nigeria.
Ryan GallagherThe Centre for Machine Vision, Bristol Robotics Laboratory, UWE Bristol, Bristol, United Kingdom.
Amir KarimiThe Centre for Machine Vision, Bristol Robotics Laboratory, UWE Bristol, Bristol, United Kingdom.
Fahim TariqSciFlair Ltd., Bristol, United Kingdom.
Melvyn L SmithThe Centre for Machine Vision, Bristol Robotics Laboratory, UWE Bristol, Bristol, United Kingdom.
Bristol Robotics Laboratory · GBUniversity of Calabar · NGUniversity of the West of England · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine vision has demonstrated its usefulness in the livestock industry in terms of improving welfare in such areas as lameness detection and body condition scoring in dairy cattle. In this article, we present some promising results of applying state of the art object detection and classification techniques to insects, specifically Black Soldier Fly (BSF) and the domestic cricket, with the view of enabling automated processing for insect farming. We also present the low-cost "Insecto" Internet of Things (IoT) device, which provides environmental condition monitoring for temperature, humidity, CO

Indexed as

black soldier flydeep learningdomestic cricketsinsect farmingmachine visionsex classification

Identifiers

PMID35242842
PMCPMC8886630
OpenAlexW4212980793

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.