Evidence map›Paper›PMID 38200761›Full record

ArticleAnimals : an open access journal from MDPI2023

Deep Learning Models to Predict Finishing Pig Weight Using Point Clouds.

Shiva Paudel, Rafael Vieira de Sousa, Sudhendu Raj Sharma, Tami Brown-Brandl

Abstract read
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In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

4 authors.

Shiva PaudelDepartment of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583-0726, USA.
Rafael Vieira de SousaDepartment of Biosystems Engineering, University of Sao Paulo, Pirassununga 13635-900, SP, Brazil.ORCID 0000-0001-5885-5368
Sudhendu Raj SharmaDepartment of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583-0726, USA.
Tami Brown-BrandlDepartment of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583-0726, USA.

Funding

Coordenação de Aperfeicoamento de Pessoal de Nível Superior 001
6 · The paper itself

Abstract

The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20-120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R

Indexed as

3D deep learningPointNetweight estimation

Identifiers

PMID38200761
PMCPMC10778518

What OpenQuestion holds

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Registered trials

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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.