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

Li, Xuan; Yu, Mengyuan; Xu, Dihong; Song, Zhao; Tan, Huifeng

doi:10.3390/agriculture13071395

Cited by 4 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model presented in this study offers several characteristics: 1. Contactless measurement: Unlike traditional contact-based methods, this study employs a non-contact approach for measurement [31]. By eliminating physical contact, this method reduces stress in pigs and minimizes the frequency of human-pig interaction, thereby lowering the risk of zoonotic diseases.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data

Liu,

Zhou,

Bian

et al. 2024

Animals

View full text Add to dashboard Cite

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire–Landrace crossbred sows. We selected 200 Yorkshire–Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs’ backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire–Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

show abstract

Section: Discussionmentioning

confidence: 99%

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data

Liu,

Zhou,

Bian

et al. 2024

Animals

View full text Add to dashboard Cite

show abstract

“…Visual inspection is the original method of sow backfat detection, supplemented by pressing, which is subjective, causes stress in sows, and makes it difficult to achieve accurate and real-time measurement [5]. In addition to the visual method, there is also the carcass method, which uses vernier calipers to measure the backfat of the carcass.…”

Section: Introductionmentioning

confidence: 99%

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

Cao,

Li,

Liu

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Aiming to address the problem that the existing methods for detecting sow backfat thickness are stressful, costly, and cannot detect in real time, this paper proposes a non-contact detection method for sow backfat with a residual network based on image segmentation using the feature visualization of neural networks. In this paper, removing the irrelevant information of the image to improve the accuracy of the sow backfat thickness detection model is proposed. The irrelevant features in the corresponding image of the feature map are found to have the same high brightness as the relevant feature regions using feature visualization. An image segmentation algorithm is then used to separate the relevant feature image regions, and the model performance before and after image segmentation is compared to verify the feasibility of this method. In order to verify the generalization ability of the model, five datasets were randomly divided, and the test results show that the coefficients of determination (R2) of the five groups were above 0.89, with a mean value of 0.91, and the mean absolute error (MAE) values were below 0.66 mm, with a mean value of 0.54 mm, indicating that the model has high detection accuracy and strong robustness. In order to explain the high accuracy of the backfat thickness detection model and to increase the credibility of the application of the detection model, using feature visualization, the irrelevant features and related features of the sow back images extracted by the residual network were statistically analyzed, which were the characteristics of the hip edge, the area near the body height point, the area near the backfat thickness measurement point (P2), and the lateral contour edge. The first three points align with the previous research on sow backfat, thus explaining the phenomenon of the high accuracy of the detection model. At the same time, the side contour edge features were found to be effective for predicting the thickness of the back. In order to explore the influence of irrelevant features on the accuracy of the model, UNet was used to segment the image area corresponding to the irrelevant features and obtain the sow contour image, which was used to construct a dorsal fat thickness detection model. The R2 results of the model were above 0.91, with a mean value of 0.94, and the MAE was below 0.65 mm, with a mean value of 0.44 mm. Compared to the test results of the model before segmentation, the average R2 of the model after segmentation increased by 3.3%, and the average MAE decreased by 18.5%, indicating that irrelevant features will reduce the detection accuracy of the model, which can provide a reference for farmers to dynamically monitor the backfat of sows and accurately manage their farms.

show abstract

“…The results of the ANN models were comparable to those given by the non-linear regression models at all growth stages. Moreover, Li et al [8] introduced a hybrid CNN-ViT (Vision Transformer, ViT) model for measuring sows' backfat thickness (BF). The model was tested on seven groups of pregnant sows (106 animals in total).…”

mentioning

confidence: 99%

Recent Advancements in Precision Livestock Farming

Liu,

Guo,

Ruchay

et al. 2023

Agriculture

View full text Add to dashboard Cite

show abstract

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

Cited by 4 publications

References 30 publications

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data

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

Recent Advancements in Precision Livestock Farming

Contact Info

Product

Resources

About