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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...

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Autores principales: Liu, Zihao, Hua, Jingyi, Xue, Hongxiang, Tian, Haonan, Chen, Yang, Liu, Haowei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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