Jiang Sheng, Zhang Guoxu, Shen Zhencai, Zhong Ping, Tan Junyan, Liu Jianfeng
College of Science, China Agricultural University, Beijing 100083, China.
National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China.
Animals (Basel). 2024 Jul 20;14(14):2122. doi: 10.3390/ani14142122.
Using computer vision technology to estimate pig live weight is an important method to realize pig welfare. But there are two key issues that affect pigs' weight estimation: one is the uneven illumination, which leads to unclear contour extraction of pigs, and the other is the bending of the pig body, which leads to incorrect pig body information. For the first one, Mask R-CNN was used to extract the contour of the pig, and the obtained mask image was converted into a binary image from which we were able to obtain a more accurate contour image. For the second one, the body length, hip width and the distance from the camera to the pig back were corrected by XGBoost and actual measured information. Then we analyzed the rationality of the extracted features. Three feature combination strategies were used to predict pig weight. In total, 1505 back images of 39 pigs obtained using Azure kinect DK were used in the numerical experiments. The highest prediction accuracy is XGBoost, with an MAE of 0.389, RMSE of 0.576, MAPE of 0.318% and R2 of 0.995. We also recommend using the Mask R-CNN + RFR method because it has fairly high precision in each strategy. The experimental results show that our proposed method has excellent performance in live weight estimation of pigs.
利用计算机视觉技术估计猪的活体重量是实现猪福利的重要方法。但存在两个影响猪体重估计的关键问题：一是光照不均匀，导致猪的轮廓提取不清晰；二是猪体弯曲，导致猪体信息错误。对于第一个问题，使用Mask R-CNN提取猪的轮廓，将得到的掩码图像转换为二值图像，从而能够获得更准确的轮廓图像。对于第二个问题，通过XGBoost和实际测量信息对体长、臀宽以及相机到猪背部的距离进行校正。然后分析提取特征的合理性。采用三种特征组合策略预测猪的体重。数值实验总共使用了利用Azure kinect DK获取的39头猪的1505张背部图像。预测精度最高的是XGBoost，平均绝对误差（MAE）为0.389，均方根误差（RMSE）为0.576，平均绝对百分比误差（MAPE）为0.318%，决定系数（R2）为0.995。我们还推荐使用Mask R-CNN + RFR方法，因为它在每种策略中都具有相当高的精度。实验结果表明，我们提出的方法在猪的活体重量估计方面具有优异的性能。
