Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, Hubei, China.
Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, Hubei, China.
Anim Genet. 2022 Dec;53(6):769-781. doi: 10.1111/age.13248. Epub 2022 Aug 21.
Since sow backfat thickness (BFT) is highly correlated with its service life and reproductive effectiveness, dynamic monitoring of BFT is a critical component of large-scale sow farm productivity. Existing contact measures of sow BFT have their problems including, high measurement intensity and sows' stress reaction, low biological safety, and difficulty in meeting the requirements for multiple measurements. This article presents a two-dimensional (2D) image-based approach for determining the BFT of pregnant sows when combined with the backfat growth rate (BGR). The 2D image features of sows extracted by convolutional neural networks (CNN) and the artificially defined phenotypic features of sows such as hip width, hip height, body length, hip height-width ratio, length-width ratio, and waist-hip ratio, were used respectively, combined with BGR, to construct a prediction model for sow BFT using support vector regression (SVR). Following testing and comparison, it was shown that using CNN to extract features from images could effectively replace artificially defined features, BGR contributed to the model's accuracy improvement. The CNN-BGR-SVR model performed the best, with R of 0.72 and mean absolute error of 1.21 mm, and root mean square error of 1.50 mm, and mean absolute percentage error of 7.57%. The results demonstrated that the CNN-BGR-SVR model based on 2D images was capable of detecting sow BFT, establishing a new reference for non-contact sow BFT detection technology.
由于母猪背部脂肪厚度（BFT）与母猪的使用寿命和繁殖效率高度相关，因此动态监测 BFT 是大规模母猪养殖场生产力的关键组成部分。现有的母猪 BFT 接触式测量方法存在一些问题，例如测量强度高、母猪应激反应大、生物安全性低，并且难以满足多次测量的要求。本文提出了一种基于二维（2D）图像的方法，结合背膘生长率（BGR）来确定怀孕母猪的 BFT。使用卷积神经网络（CNN）提取母猪的 2D 图像特征，并结合人工定义的母猪表型特征，如臀宽、臀高、体长、臀高宽比、长宽比和腰臀比，与 BGR 一起，使用支持向量回归（SVR）构建母猪 BFT 的预测模型。经过测试和比较，结果表明，使用 CNN 从图像中提取特征可以有效地替代人工定义的特征，BGR 有助于提高模型的准确性。CNN-BGR-SVR 模型表现最佳，R 值为 0.72，平均绝对误差为 1.21mm，均方根误差为 1.50mm，平均绝对百分比误差为 7.57%。结果表明，基于 2D 图像的 CNN-BGR-SVR 模型能够检测母猪 BFT，为非接触式母猪 BFT 检测技术提供了新的参考。
