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Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network
The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized b...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537768/ https://www.ncbi.nlm.nih.gov/pubmed/37765787 http://dx.doi.org/10.3390/s23187730 |
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author | Liu, Zihao Hua, Jingyi Xue, Hongxiang Tian, Haonan Chen, Yang Liu, Haowei |
author_facet | Liu, Zihao Hua, Jingyi Xue, Hongxiang Tian, Haonan Chen, Yang Liu, Haowei |
author_sort | Liu, Zihao |
collection | PubMed |
description | The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding. |
format | Online Article Text |
id | pubmed-10537768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105377682023-09-29 Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network Liu, Zihao Hua, Jingyi Xue, Hongxiang Tian, Haonan Chen, Yang Liu, Haowei Sensors (Basel) Article The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding. MDPI 2023-09-07 /pmc/articles/PMC10537768/ /pubmed/37765787 http://dx.doi.org/10.3390/s23187730 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Zihao Hua, Jingyi Xue, Hongxiang Tian, Haonan Chen, Yang Liu, Haowei Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network |
title | Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network |
title_full | Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network |
title_fullStr | Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network |
title_full_unstemmed | Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network |
title_short | Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network |
title_sort | body weight estimation for pigs based on 3d hybrid filter and convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537768/ https://www.ncbi.nlm.nih.gov/pubmed/37765787 http://dx.doi.org/10.3390/s23187730 |
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