Non‐contact detection method of pregnant sows backfat thickness based on two‐dimensional images

Abstract

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 R2 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.

Research on Contactless Detection of Sow Backfat Thickness Based on Segmented Images with Feature Visualization

“…In order to solve the problems of time consumption, relative inefficiency, equipment constraints, and the influence of surveyors, this study proposes a non-contact detection method for measuring sow backfat thickness based on the feature visualization of a residual network. Compared to the VGG16 model and that proposed by YU M et al [31], the R 2 of the model in this study was 0.94 in the test set, which is higher than the R 2 of the other two models, and the MAE was 0.44 mm, which is lower than the MAE of the other two models, indicating that the performance of the model in this study is better than the other two. Secondly, compared to the model before segmentation, the R 2 increased by 3.3% and the MAE decreased by 18.5%, which verifies the feasibility of removing irrelevant features and improving the accuracy of the sow backfat thickness detection model.…”

“…VGG16 had strong performance in the classification field [30] but it did not perform very well in this study, with an R 2 of 0.66 and an MAE of 0.66 mm, which were the lowest and the highest, respectively. In this study, the R 2 increased by 42.4% and 23.6%, respectively, and the MAE decreased by 68.3% and 63.3% compared to VGG16 and the model by YU M et al [31], respectively. Therefore, the performance of the model in this study was better than VGG16 and that proposed by YU M et al [31].…”

“…In this study, the R 2 increased by 42.4% and 23.6%, respectively, and the MAE decreased by 68.3% and 63.3% compared to VGG16 and the model by YU M et al [31], respectively. Therefore, the performance of the model in this study was better than VGG16 and that proposed by YU M et al [31]. 3 and 11.…”

Non-Contact Measurement of Pregnant Sows’ Backfat Thickness Based on a Hybrid CNN-ViT Model

“…Point cloud data or a depth map can be used to acquire more dimensional information, but their accuracy will be affected by the environment with limited scenarios and higher costs [15,16]. Yu et al [17] constructed a CNN-BGR-SVR model to measure the BF of pregnant sows based on 2D images of the sows' backs and used BGR features that took into account the heritability of BF. The study showed that the BF could be non-contact measured using 2D back images.…”