Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network
In order to avoid damaging viscera during poultry evisceration and enhance the economic value of poultry products, this paper proposes a predictive method for poultry carcass visceral dimensions based on 3D point cloud and a Genetic Algorithm-based Wavelet Neural Network (GA-WNN). In this study, a d...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Poultry Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0032579124010940 |
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| author | Zhengwei Zhu Yan Chen Lu Cai Jinzhou Yang Ke Wen Jingjing Bao Zhigang Hu Dandan Fu |
| author_facet | Zhengwei Zhu Yan Chen Lu Cai Jinzhou Yang Ke Wen Jingjing Bao Zhigang Hu Dandan Fu |
| author_sort | Zhengwei Zhu |
| collection | DOAJ |
| description | In order to avoid damaging viscera during poultry evisceration and enhance the economic value of poultry products, this paper proposes a predictive method for poultry carcass visceral dimensions based on 3D point cloud and a Genetic Algorithm-based Wavelet Neural Network (GA-WNN). In this study, a data set of poultry carcasses was obtained through the use of 3D point cloud scanning equipment combined with reverse engineering software. The inputs and predicted targets of the model were determined through correlation analysis of various carcass dimensions. Then, a prediction model of poultry visceral size (GA-WNN) was built by K-fold cross validation method, Genetic Algorithm and Wavelet Neural Network (WNN). By comparing the prediction results and analyzing Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of the six models, it was determined that the GA-WNN model had the best prediction results. Finally, in order to verify the generalizability of the method, generalizability experiments were conducted on different breeds of poultry, which proved that the method of this study had superior generalizability ability. In the comparative analysis of the six models, the MAPE and RMSE of the GA-WNN model for the prediction of the three visceral dimensions were the lowest except for the RMSE for the prediction of visceral length. Compared with the largest of the two kinds of errors, the MAPE and RMSE for the prediction of the position of the upper end of the left liver by the method of this study were lower by 5.56% and 0.915 cm, respectively, and the prediction effect had a significant advantage. The experimental results showed that the model built in this paper based on 3D point cloud and GA-WNN network can accurately predict the size of the viscera of poultry carcasses, thus providing theoretical references for the automated evisceration technology without damaging the viscera. |
| format | Article |
| id | doaj-art-3a728cf2e2844dd19b839432d642b7bc |
| institution | Kabale University |
| issn | 0032-5791 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj-art-3a728cf2e2844dd19b839432d642b7bc2025-01-22T05:40:17ZengElsevierPoultry Science0032-57912025-01-011041104516Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural networkZhengwei Zhu0Yan Chen1Lu Cai2Jinzhou Yang3Ke Wen4Jingjing Bao5Zhigang Hu6Dandan Fu7College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaCorresponding author.; College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaCollege of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaCollege of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaCollege of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaCollege of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaCollege of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaCollege of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, ChinaIn order to avoid damaging viscera during poultry evisceration and enhance the economic value of poultry products, this paper proposes a predictive method for poultry carcass visceral dimensions based on 3D point cloud and a Genetic Algorithm-based Wavelet Neural Network (GA-WNN). In this study, a data set of poultry carcasses was obtained through the use of 3D point cloud scanning equipment combined with reverse engineering software. The inputs and predicted targets of the model were determined through correlation analysis of various carcass dimensions. Then, a prediction model of poultry visceral size (GA-WNN) was built by K-fold cross validation method, Genetic Algorithm and Wavelet Neural Network (WNN). By comparing the prediction results and analyzing Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of the six models, it was determined that the GA-WNN model had the best prediction results. Finally, in order to verify the generalizability of the method, generalizability experiments were conducted on different breeds of poultry, which proved that the method of this study had superior generalizability ability. In the comparative analysis of the six models, the MAPE and RMSE of the GA-WNN model for the prediction of the three visceral dimensions were the lowest except for the RMSE for the prediction of visceral length. Compared with the largest of the two kinds of errors, the MAPE and RMSE for the prediction of the position of the upper end of the left liver by the method of this study were lower by 5.56% and 0.915 cm, respectively, and the prediction effect had a significant advantage. The experimental results showed that the model built in this paper based on 3D point cloud and GA-WNN network can accurately predict the size of the viscera of poultry carcasses, thus providing theoretical references for the automated evisceration technology without damaging the viscera.http://www.sciencedirect.com/science/article/pii/S0032579124010940Poultry viscera3D point cloudGenetic algorithm-based wavelet neural networkMean absolute percentage errorRoot mean square error |
| spellingShingle | Zhengwei Zhu Yan Chen Lu Cai Jinzhou Yang Ke Wen Jingjing Bao Zhigang Hu Dandan Fu Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network Poultry Science Poultry viscera 3D point cloud Genetic algorithm-based wavelet neural network Mean absolute percentage error Root mean square error |
| title | Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network |
| title_full | Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network |
| title_fullStr | Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network |
| title_full_unstemmed | Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network |
| title_short | Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network |
| title_sort | predictive method for poultry carcass visceral dimensions using 3d point cloud and genetic algorithm based wavelet neural network |
| topic | Poultry viscera 3D point cloud Genetic algorithm-based wavelet neural network Mean absolute percentage error Root mean square error |
| url | http://www.sciencedirect.com/science/article/pii/S0032579124010940 |
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