Determination of the Live Weight of Farm Animals with Deep Learning and Semantic Segmentation Techniques
Source abstract
Abstract
In cattle breeding, regularly taking the animals to the scale and recording their weight is important for both the performance of the enterprise and the health of the animals. This process, which must be carried out in businesses, is a difficult task. For this reason, it is often not performed regularly or not performed at all. In this study, we attempted to estimate the weights of cattle by using stereo vision and semantic segmentation methods used in the field of computer vision together. Images of 85 animals were taken from different angles with a stereo setup consisting of two identical cameras. The distances of the animals to the camera plane were calculated by stereo distance calculation, and the areas covered by the animals in the images were determined by semantic segmentation methods. Then, using all these data, different artificial neural network models were trained. As a result of the study, it was revealed that when stereo vision and semantic segmentation methods are used together, live animal weights can be predicted successfully.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Effects of Environmental Stressors on Livestock · Animal Behavior and Welfare Studies · Meat and Animal Product Quality
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Faculty of Engineering Natural Sciences, Maltepe University, 34857 Istanbul, Turkey
References (38)
Show source-provided references
1. Kaya, M. (2019). Laktasyondaki Holştayn Ineklerde Canlı Ağırlık ve Beden Kondisyon Skorunun Sayısal Görüntü Analizi Yöntemi ile Belirlenebilirliği. [Ph.D. Thesis, Aydın Adnan Menderes University].
2. Dang, C., Choi, T., Lee, S., Lee, S., Alam, M., Park, M., Han, S., Lee, J., and Hoang, D. (2022). Machine Learning-Based Live Weight Estimation for Hanwoo Cow. Sustainability, 14.10.3390/su141912661 ↗
3. ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images10.1093/jas/skab022 ↗
4. Na, M.H., Cho, W.H., Kim, S.K., and Na, I.S. (2022). Automatic weight prediction system for Korean cattle using Bayesian ridge algorithm on RGB-D image. Electronics, 11.10.3390/electronics11101663 ↗
5. Vindis, P., Brus, M., Stajnko, D., and Janzekovic, M. (2010). New Trends in Technologies: Control, Management, Computational Intelligence and Network Systems, IntechOpen.
6. Wang, Q. (2018, January 18–20). A Body Measurement Method Based on the Ultrasonic Sensor. Proceedings of the 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET), Beijing, China.10.1109/CCET.2018.8542208 ↗
7. Cattle weight estimation using active contour models and regression trees Bagging10.1016/j.compag.2020.105804 ↗
8. ANN approach for estimation of cow weight depending on photogrammetric body dimensions10.26833/ijeg.427531 ↗
9. Walk-through weighing of pigs using machine vision and an artificial neural network10.1016/j.biosystemseng.2007.08.008 ↗
10. Anifah, L. (2021, January 9–11). Decision Support System Two Dimensional Cattle Weight Estimation using Fuzzy Rule Based System. Proceedings of the 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), Surabaya, Indonesia.10.1109/EIConCIT50028.2021.9431911 ↗
11. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device10.1016/j.compind.2018.02.011 ↗
12. A novel automated system to acquire biometric and morphological measurements and predict body weight of pigs via 3D computer vision10.1093/jas/sky418 ↗
13. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases10.1016/j.livsci.2019.103904 ↗
14. Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements10.1016/j.livsci.2020.104054 ↗
15. 3D Computer-vision system for automatically estimating heifer height and body mass10.1016/j.biosystemseng.2017.11.014 ↗
16. Automated body weight prediction of dairy cows using 3-dimensional vision10.3168/jds.2017-13094 ↗
17. On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera10.1016/j.compag.2018.03.003 ↗
18. Volume and surface area of Holstein dairy cows calculated from complete 3D shapes acquired using a high-precision scanning system: Interest for body weight estimation10.1016/j.compag.2019.104977 ↗
19. Estimation of bull live weight through thermographically measured body dimensions10.1016/j.compag.2007.12.002 ↗
20. An approach of pig weight estimation using binocular stereo system based on LabVIEW10.1016/j.compag.2016.08.012 ↗
21. Nishide, R., Yamashita, A., Takaki, Y., Ohta, C., Oyama, K., and Ohkawa, T. (2018, January 6–7). Calf robust weight estimation using 3D contiguous cylindrical model and directional orientation from stereo images. Proceedings of the Ninth International Symposium on Information and Communication Technology, Danang City, Vietnam.10.1145/3287921.3287923 ↗
22. Yamashita, A., Ohkawa, T., Oyama, K., Ohta, C., Nishide, R., and Honda, T. (2017, January 27–31). Estimation of calf weight from fixed-point stereo camera images using three-dimensional successive cylindrical model. Proceedings of the 5th IIAE International Conference on Intelligent Systems and Image Processing, Kitakyushu, Japan.10.12792/icisip2017.046 ↗
23. Image-based body mass prediction of heifers using deep neural networks10.1016/j.biosystemseng.2021.02.001 ↗
24. An intelligent pig weights estimate method based on deep learning in sow stall environments10.1109/ACCESS.2019.2953099 ↗
25. Suwannakhun, S., and Daungmala, P. (2018, January 26–29). Estimating pig weight with digital image processing using deep learning. Proceedings of the 2018 14th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), Las Palmas de Gran Canaria, Spain.10.1109/SITIS.2018.00056 ↗
26. Target-free calibration of flat refractive imaging systems using two-view geometry10.1016/j.optlaseng.2021.106856 ↗
27. Stereo disparity optimization with depth change constraint based on a continuous video10.1016/j.displa.2021.102073 ↗
28. Shete, P.P., Sarode, D.M., and Bose, S.K. (2014, January 24–27). A real-time stereo rectification of high definition image stream using GPU. Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India.10.1109/ICACCI.2014.6968218 ↗
29. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.322 ↗
30. Long, J., Shelhamer, E., and Darrell, T. (2015, January 7–12). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298965 ↗
31. Modality specific U-Net variants for biomedical image segmentation: A survey10.1007/s10462-022-10152-1 ↗
32. A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net10.1016/j.compag.2021.106373 ↗
33. Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review10.1016/j.wasman.2021.02.029 ↗
34. Gunasegaran, T., and Cheah, Y.N. (2017, January 17–18). Evolutionary cross validation. Proceedings of the 2017 8th International Conference on Information Technology (ICIT), Amman, Jordan.10.1109/ICITECH.2017.8079960 ↗
35. Dependency Analysis of Accuracy Estimates in k-Fold Cross Validation10.1109/TKDE.2017.2740926 ↗
36. Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis10.1016/j.jocm.2018.07.002 ↗
37. A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals10.17678/beuscitech.338085 ↗
38. Artificial neural networks: Fundamentals, computing, design, and application10.1016/S0167-7012(00)00201-3 ↗