REFERENCES

1. Frost, A.; Schofield, C.; Beaulah, S.; Mottram, T.; Lines, J.; Wathes, C. A review of livestock monitoring and the need for integrated systems. Comput. Electron. Agric. 1997, 17, 139-59.

2. Zin, T. T.; Seint, P. T.; Tin, P.; Horii, Y.; Kobayashi, I. Body condition score estimation based on regression analysis using a 3D camera. Sensors 2020, 20, 3705.

3. Imamura, S.; Zin, T. T.; Kobayashi, I.; Horii, Y. Automatic evaluation of Cow’s body-condition-score using 3D camera. In Proceedings of the 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), Nagoya, Japan. Oct 24-27, 2017. IEEE; 2017. pp. 1-2.

4. Franchi, G. A.; Bus, J. D.; Boumans, I. J.; Bokkers, E. A.; Jensen, M. B.; Pedersen, L. J. Estimating body weight in conventional growing pigs using a depth camera. Smart. Agric. Technol. 2023, 3, 100117.

5. Lee, J.; Jin, L.; Park, D.; Chung, Y. Automatic recognition of aggressive behavior in pigs using a kinect depth sensor. Sensors 2016, 16, 631.

6. Hu, Z.; Yang, H.; Lou, T. Dual attention-guided feature pyramid network for instance segmentation of group pigs. Comput. Electron. Agric. 2021, 186, 106140.

7. Peng, W.; Liu, Z.; Cai, J.; Zhao, Y. Research and application progress of electronic ear tags as infrastructure for precision livestock industry: a review. Intell. Robot. 2025, 5, 433-49.

8. Bhoj, S.; Tarafdar, A.; Chauhan, A.; Singh, M.; Gaur, G. K. Image processing strategies for pig liveweight measurement: updates and challenges. Comput. Electron. Agric. 2022, 193, 106693.

9. Dohmen, R.; Catal, C.; Liu, Q. Computer vision‐based weight estimation of livestock: a systematic literature review. N. Z. J. Agric. Res. 2022, 65, 227-47.

10. Sun, Y.; Li, Q.; Ma, W.; et al. A multi-view real-time approach for rapid point cloud acquisition and reconstruction in goats. Agriculture 2024, 14, 1785.

11. Xie, Q.; Zhou, H.; Bao, J.; Da, Q. Advances in machine vision-driven body weight assessment for livestock and poultry. J. Agric. Mach. 2022, 53, 1-15.

12. Alenyà, G.; Foix, S.; Torras, C. ToF cameras for active vision in robotics. Sens. Actuators. A. Phys. 2014, 218, 10-22.

13. Huang, L.; Li, S.; Zhu, A.; Fan, X.; Zhang, C.; Wang, H. Non-contact body measurement for Qinchuan cattle with LiDAR sensor. Sensors 2018, 18, 3014.

14. Quintana Benito, J.; Fernández-Balbuena, A. A.; Martínez-Antón, J. C.; Váquez Molini, D. Improvement of driver night vision in foggy environments by structured light projection. Heliyon 2022, 8, e11877.

15. Zhu, J.; Zeng, Q.; Han, F.; Jia, C.; Bian, Y.; Wei, C. Design of laser scanning binocular stereo vision imaging system and target measurement. Optik 2022, 270, 169994.

16. Ma, W.; Qi, X.; Sun, Y.; et al. Computer vision-based measurement techniques for livestock body dimension and weight: a review. Agriculture 2024, 14, 306.

17. Wang, Z. Review of real-time three-dimensional shape measurement techniques. Measurement 2020, 156, 107624.

18. Ruchay, A.; Kober, V.; Dorofeev, K.; Kolpakov, V.; Miroshnikov, S. Accurate body measurement of live cattle using three depth cameras and non-rigid 3-D shape recovery. Comput. Electron. Agric. 2020, 179, 105821.

19. Condotta, I. C.; Brown-Brandl, T. M.; Silva-Miranda, K. O.; Stinn, J. P. Evaluation of a depth sensor for mass estimation of growing and finishing pigs. Biosyst. Eng. 2018, 173, 11-8.

20. Ruchay, A.; Kolpakov, V.; Gerasimov, N.; et al. Ultrasound and optical measurement data fusion for assessing the morphological traits and weight of Aberdeen Angus cattle. Comput. Electron. Agric. 2025, 233, 110203.

21. Pezzuolo, A.; Milani, V.; Zhu, D.; Guo, H.; Guercini, S.; Marinello, F. On-barn pig weight estimation based on body measurements by structure-from-motion (SfM). Sensors 2018, 18, 3603.

22. Kongsro, J. Estimation of pig weight using a Microsoft Kinect prototype imaging system. Comput. Electron. Agric. 2014, 109, 32-5.

23. Marchant, J. A.; Schofield, C. P.; White, R. P. Pig growth and conformation monitoring using image analysis. Anim. Sci. 1999, 68, 141-50.

24. Kashiha, M.; Bahr, C.; Ott, S.; et al. Automatic weight estimation of individual pigs using image analysis. Comput. Electron. Agric. 2014, 107, 38-44.

25. Wu, J.; Tillett, R.; Mcfarlane, N.; Ju, X.; Siebert, J.; Schofield, P. Extracting the three-dimensional shape of live pigs using stereo photogrammetry. Comput. Electron. Agric. 2004, 44, 203-22.

26. Shuai, S.; Ling, Y.; Shihao, L.; et al. Research on 3D surface reconstruction and body size measurement of pigs based on multi-view RGB-D cameras. Comput. Electron. Agric. 2020, 175, 105543.

27. Wu, Z.; Zhang, J.; Li, J.; Zhao, W. Multi-view fusion-based automated full-posture cattle body size measurement. Animals 2024, 14, 3190.

28. Liu, T.; Teng, G.; Zhang, S.; Li, Z.; Guo, P. Surface reconstruction and applications of pig body from point cloud data. J. Agric. Mach. 2014, 45, 291-5.

29. Ji, X.; Li, Q.; Guo, K.; et al. A machine learning-based method for pig weight estimation and the PIGRGB-weight dataset. Agriculture 2025, 15, 814.

30. Jun, K.; Kim, S. J.; Ji, H. W. Estimating pig weights from images without constraint on posture and illumination. Comput. Electron. Agric. 2018, 153, 169-76.

31. Xu, Z.; Li, Q.; Ma, W.; Li, M.; Xue, X.; Zhao, C. A reconstruction method for incomplete pig point clouds based on stepwise hole filling and its applications. Biosyst. Eng. 2025, 255, 104171.

32. Du, A.; Guo, H.; Lu, J.; et al. Automatic livestock body measurement based on keypoint detection with multiple depth cameras. Comput. Electron. Agric. 2022, 198, 107059.

33. Harvard Dataverse. Pigs_weight. 2024. https://doi.org/10.7910/DVN/AZ5IBM. (accessed 2026-04-27).

34. Kaggle. Sheep weight estimation dataset. https://www.kaggle.com/datasets/tianfanghzau/sheep-weight-estimation-dataset-multimodal-images. (accessed 2026-04-27).

35. Wang, Y.; Yuan, X.; Wei, B.; Ruchay, A.; Pezzuolo, A.; Guo, H. Performance evaluation of a state-of-the-art keypoint detection method for precision livestock farming. Comput. Electron. Agric. 2026, 240, 111230.

36. Wang, Z.; Tulpan, D.; Bergeron, R. PSI-16 Estimation of pigs live body weight from digital images using reference objects. J. Anim. Sci. 2021, 99, 276-7.

37. Gao, Y.; Guo, J.; Xuan, L.; Lei, M.; Lu, J.; Tong, Y. Deep learning-driven instance segmentation for group-housed pigs in agricultural images. J. Agric. Mach. 2019, 50, 179-87.

38. Han, H.; Xue, X.; Li, Q.; et al. Pig-ear detection from the thermal infrared image based on improved YOLOv8n. Intell. Robot. 2024, 4, 1-19.

39. Papadakis, P.; Pratikakis, I.; Perantonis, S.; Theoharis, T. Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation. Pattern. Recogn. 2007, 40, 2437-52.

40. Liu, Z.; Hua, J.; Xue, H.; Tian, H.; Chen, Y.; Liu, H. Body weight estimation for pigs based on 3D hybrid filter and convolutional neural network. Sensors 2023, 23, 7730.

41. Zhang, M.; Zhang, L.; Takis Mathiopoulos, P.; Ding, Y.; Wang, H. Perception-based shape retrieval for 3D building models. ISPRS. J. Photogramm. Remote. Sens. 2013, 75, 76-91.

42. Lu, J.; Guo, H.; Du, A.; et al. 2-D/3-D fusion-based robust pose normalisation of 3-D livestock from multiple RGB-D cameras. Biosyst. Eng. 2022, 223, 129-41.

43. Wang, K.; Guo, H.; Liu, W.; Ma, Q.; Su, W.; Zhu, D. Extraction method of pig body size measurement points based on rotation normalization of point cloud. Trans. Chin. Soc. Agric. Eng. 2017, 33, 253-9.

44. Guo, H.; Li, Z.; Ma, Q.; et al. A bilateral symmetry based pose normalization framework applied to livestock body measurement in point clouds. Comput. Electron. Agric. 2019, 160, 59-70.

45. Li, J.; Ma, W.; Li, Q.; Xue, X.; Wang, Z. Automatic acquisition and target extraction of beef cattle 3D point cloud from complex environment. Smart. Agric. 2022, 4, 64-76.

46. Yin, L.; Cai, G.; Tian, X.; et al. 3D point cloud reconstruction and morphometric measurement of pig bodies using multi-view depth cameras. Trans. Chin. Soc. Agric. Eng. 2019, 35, 201-8.

47. Jin, B.; Wang, G.; Feng, J.; et al. PointStack based 3D automatic body measurement for goat phenotypic information acquisition. Biosyst. Eng. 2024, 248, 32-46.

48. Wang, S.; Jiang, H.; Qiao, Y.; Jiang, S. A method for obtaining 3D point cloud data by combining 2D image segmentation and depth information of pigs. Animals 2023, 13, 2472.

49. Kazhdan, M.; Bolitho, M.; Hoppe, H. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, Cagliari, Italy; 2006; pp. 61-70. https://hhoppe.com/poissonrecon.pdf. (accessed 2026-04-27).

50. Bernardini, F.; Mittleman, J.; Rushmeier, H.; Silva, C.; Taubin, G. The ball-pivoting algorithm for surface reconstruction. IEEE. Trans. Vis. Comput. Graph. 1999, 5, 349-59.

51. Curless, B.; Levoy, M. A volumetric method for building complex models from range images. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, New Orleans, USA; 1996; pp. 303-12. https://graphics.stanford.edu/papers/volrange/volrange.pdf. (accessed 2026-04-27).

52. Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; Ng, R. NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM. 2022, 65, 99-106.

53. Kerbl, B.; Kopanas, G.; Leimkuehler, T.; Drettakis, G. 3D Gaussian Splatting for real-time radiance field rendering. ACM. Trans. Graph. 2023, 42, 1-14.

54. Wang, Y.; Huang, T.; Chen, H.; Lee, G. H. FreeSplat: generalizable 3D Gaussian Splatting towards free view synthesis of indoor scenes. arXiv 2024; arXiv:2405.17958. Available online: https://doi.org/10.48550/arXiv.2405.17958. (accessed 27 Apr 2026).

55. Nguyen, A. H.; Holt, J. P.; Knauer, M. T.; Abner, V. A.; Lobaton, E. J.; Young, S. N. Towards rapid weight assessment of finishing pigs using a handheld, mobile RGB-D camera. Biosyst. Eng. 2023, 226, 155-68.

56. Hakem, M.; Boulouard, Z.; Kissi, M. Classification of body weight in beef cattle via machine learning methods: a review. Procedia. Comput. Sci. 2022, 198, 263-8.

57. Tasdemir, S.; Urkmez, A.; Inal, S. Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Comput. Electron. Agric. 2011, 76, 189-97.

58. Al Ard Khanji, M. S.; Llorente, C.; Falceto, M. V.; et al. Using body measurements to estimate body weight in gilts. Can. J. Anim. Sci. 2018, 98, 362-7.

59. Wang, Y.; Yang, W.; Winter, P.; Walker, L. Walk-through weighing of pigs using machine vision and an artificial neural network. Biosyst. Eng. 2008, 100, 117-25.

60. Dang, C.; Choi, T.; Lee, S.; et al. Machine learning-based live weight estimation for Hanwoo cow. Sustainability 2022, 14, 12661.

61. Norouzzadeh, M. S.; Nguyen, A.; Kosmala, M.; et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. U. S. A. 2018, 115, E5716-25.

62. Meckbach, C.; Tiesmeyer, V.; Traulsen, I. A promising approach towards precise animal weight monitoring using convolutional neural networks. Comput. Electron. Agric. 2021, 183, 106056.

63. Ruchay, A.; Kober, V.; Dorofeev, K.; Kolpakov, V.; Gladkov, A.; Guo, H. Live weight prediction of cattle based on deep regression of RGB-D images. Agriculture 2022, 12, 1794.

64. Kwon, K.; Park, A.; Lee, H.; Mun, D. Deep learning-based weight estimation using a fast-reconstructed mesh model from the point cloud of a pig. Comput. Electron. Agric. 2023, 210, 107903.

65. Li, G.; Liu, X.; Ma, Y.; Wang, B.; Zheng, L.; Wang, M. Body size measurement and live body weight estimation for pigs based on back surface point clouds. Biosyst. Eng. 2022, 218, 10-22.

66. Bai, L.; Guo, C.; Song, J. Cattle weight estimation model through readily photos. Eng. Appl. Artif. Intell. 2025, 143, 109976.

67. Zhu, J.; Chen, Z.; Yin, L.; et al. Posture standardization of pig point cloud based on skeleton extraction and transformation. Int. J. Agric. Biol. Eng. 2025, 18, 83-91.

68. Lu, Z.; Liao, Y.; Li, J. Translation-based multimodal learning: a survey. Intell. Robot. 2025, 5, 783-804.

69. Dong, X.; Zhang, C.; Wang, P.; et al. A novel dual-network approach for real-time liveweight estimation in precision livestock management. Adv. Sci. 2025, 12, e2417682.

70. Yang, L.; Jiang, T.; Gui, X.; Duan, Q. Automated body measurement of beef cattle based on keypoint detection and local point cloud clustering. Meas. Sci. Technol. 2024, 35, 126013.

71. He, W.; Mi, Y.; Ding, X.; Liu, G.; Li, T. Two-stream cross-attention vision transformer based on RGB-D images for pig weight estimation. Comput. Electron. Agric. 2023, 212, 107986.

72. Hyndman, R. J.; Koehler, A. B. Another look at measures of forecast accuracy. Int. J. Forecast. 2006, 22, 679-88.

73. Chicco, D.; Warrens, M. J.; Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ. Comput. Sci. 2021, 7, e623.

74. Chaaban, C. R.; Berry, N. T.; Armitano-Lago, C.; Kiefer, A. W.; Mazzoleni, M. J.; Padua, D. A. Combining inertial sensors and machine learning to predict vGRF and knee biomechanics during a double limb jump landing task. Sensors 2021, 21, 4383.

75. Tu, G. J.; Jørgensen, E. Vision analysis and prediction for estimation of pig weight in slaughter pens. Expert. Syst. Appl. 2023, 220, 119684.

76. Dohmen, R.; Catal, C.; Liu, Q. Image-based body mass prediction of heifers using deep neural networks. Biosyst. Eng. 2021, 204, 283-93.

77. Wei, Y.; Zhang, L.; Yang, F.; et al. Automatic measurement method of sheep body size based on 3D reconstruction and point cloud segmentation. Comput. Electron. Agric. 2025, 239, 110978.

78. Chu, M.; Liu, G.; Si, Y.; Feng, F. Predicting method of dairy cow weight based on three-dimensional reconstruction. Trans. Chin. Soc. Agric. Mach. 2020, 51, 378-84.

79. Thapar, G.; Biswas, T. K.; Bhushan, B.; et al. Accurate estimation of body weight of pigs through smartphone image measurement app. Smart. Agric. Technol. 2023, 4, 100194.

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