Pig weight and body size are important indicators for producers. Due to the increasing scale of pig farms, it is increasingly difficult for farmers to quickly and automatically obtain pig weight and body size. Due to this problem, we focused on a multiple output regression convolutional neural network (CNN) to estimate pig weight and body size. DenseNet201, ResNet152 V2, Xception and MobileNet V2 were modified into multiple output regression CNNs and trained on modeling data. By comparing the estimated performance of each model on test data, modified Xception was selected as the optimal estimation model. Based on pig height, body shape, and contour, the mean absolute error (MAE) of the model to estimate body weight (BW), shoulder width (SW), shoulder height (SH), hip width (HW), hip width (HH), and body length (BL) were 1.16 kg, 0.33 cm, 1.23 cm, 0.38 cm, 0.66 cm, and 0.75 cm, respectively. The coefficient of determination (R²) value between the estimated and measured results was in the range of 0.9879–0.9973. Combined with the LabVIEW software development platform, this method can estimate pig weight and body size accurately, quickly, and automatically. This work contributes to the automatic management of pig farms.

中文翻译：

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一种快速，全自动的方法

猪的体重和体重是生产者的重要指标。由于养猪场规模的扩大，农民越来越难以快速，自动地获取猪的体重和体型。由于这个问题，我们专注于多输出回归卷积神经网络（CNN）来估算猪的体重和体型。将DenseNet201，ResNet152 V2，Xception和MobileNet V2修改为多个输出回归CNN，并在建模数据上进行训练。通过比较每个模型在测试数据上的估计性能，选择了改进的Xception作为最佳估计模型。根据猪的身高，身体形状和轮廓，模型的平均绝对误差（MAE）可以估算体重（BW），肩宽（SW），肩高（SH），hip骨宽度（HW），hip骨宽度（ HH）和身长（BL）分别为1.16公斤，0.33厘米，1.23厘米，分别为0.38厘米，0.66厘米和0.75厘米。测定系数（R²）估计结果与测量结果之间的值在0.9879-0.9973之间。结合LabVIEW软件开发平台，该方法可以准确，快速，自动地估算猪的体重和体型。这项工作有助于养猪场的自动管理。