Zhu Zhengwei, Chen Yan, Cai Lu, Yang Jinzhou, Wen Ke, Bao Jingjing, Hu Zhigang, Fu Dandan
College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, China.
College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, China.
Poult Sci. 2025 Jan;104(1):104516. doi: 10.1016/j.psj.2024.104516. Epub 2024 Nov 6.
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.
为避免家禽去内脏过程中损伤内脏并提高禽产品的经济价值，本文提出一种基于三维点云及基于遗传算法的小波神经网络（GA-WNN）的家禽胴体内脏尺寸预测方法。本研究通过使用三维点云扫描设备结合逆向工程软件获得了家禽胴体数据集。通过对各种胴体尺寸进行相关性分析确定了模型的输入和预测目标。然后，采用K折交叉验证法、遗传算法和小波神经网络（WNN）建立了家禽内脏大小预测模型（GA-WNN）。通过比较预测结果并分析六个模型的平均绝对百分比误差（MAPE）和均方根误差（RMSE），确定GA-WNN模型具有最佳预测结果。最后，为验证该方法的通用性，对不同品种家禽进行了通用性实验，证明本研究方法具有卓越的通用能力。在六个模型的对比分析中，GA-WNN模型对三个内脏尺寸预测的MAPE和RMSE除内脏长度预测的RMSE外均为最低。与两种误差中最大的相比，本研究方法对左肝上端位置预测的MAPE和RMSE分别低5.56%和0.915厘米，预测效果具有显著优势。实验结果表明，本文基于三维点云和GA-WNN网络建立的模型能够准确预测家禽胴体内脏大小，从而为无损内脏的自动化去内脏技术提供理论参考。
