Robust 3D Pig Measurement in Pig Farm
Abstract
On a pig farm, the shipment of pigs of proper weight is very important for increasing profit. However, in order to reduce labor costs, many farmers ship pigs without weighing them. Therefore, an automatic sorting system that selects pigs that have reached the proper weight by measuring the weight of each pig has been developed. In the present paper, a weight estimation system using a camera for pig sorting is introduced. Three-dimensional visual information on a pig captured in a single image is used to estimate its weight. The proposed method is robust and practical for the measurement of a moving animal in a poor environment of pig farms.
Keywords
Pig measurement Computer vision Three dimensional Weight estimation Multiple slits laser Random dotsNotes
Acknowledgement
All animal experiments were conducted in compliance with the protocol which was reviewed by the Institutional Animal Care and Use Committee and approved by the President of University of Miyazaki (Permit Number: 2017-021).
This research was supported by grants from the Project of the NARO Bio-oriented Technology Research Advancement Institution (the special scheme project on vitalizing management entities of agriculture, forestry and fisheries).
References
- 1.Lee, S.Y., Song, C.H., Choe, Y.C.: The convergence of ICT and automatic sorting system: a quantitative performance analysis. Adv. Sci. Technol. Lett. 49(1), 229–235 (2014)CrossRefGoogle Scholar
- 2.Salak-Johnson, J.L.: Impact of auto-sort systems on pig welfare, vol. 31, no. 2, pp. 1–17. North Carolina Swine Extension (2008)Google Scholar
- 3.Kashiha, M., Bahr, C., Ott, S., Moons, C.P., Niewold, T.A.: Automatic weight estimation of individual pigs using image analysis. Comput. Electron. Agric. 107, 38–44 (2014)CrossRefGoogle Scholar
- 4.Schofield, C., Marchant, J., White, R., Brandl, N., Wilson, M.: Monitoring pig growth using a prototype imaging system. J. Agric. Eng. Res. 72(3), 205–210 (1999)CrossRefGoogle Scholar
- 5.Wang, Y., Yang, W., Winter, P., Walkder, L.: Walk-through weighing of pigs using machine vision and an artificial neural network. Biosyst. Eng. 100(1), 117–125 (2008)CrossRefGoogle Scholar
- 6.Asai, T., Ueyama, K., Yamanae, T., Maruyama, M., Segaki, H.: A simpler estimating method of the body weight for growing finishing swine of large type breed (in Japanese). Jpn. Pork J. 6(1), 1–5 (1969)Google Scholar
- 7.Nadiope, G., Stock, J., Stalder, K.J., Pezo, D.: Prediction of live body weight using various body measurements in Ugandan village pigs. Livest. Res. Rural. Dev. 26(5), 1–7 (2014)Google Scholar
- 8.Geng, Z.J.: Rainbow three dimensional camera: new concept of high speed three dimensional vision systems. Opt. Eng. 35(2), 376–383 (1996)CrossRefGoogle Scholar
- 9.Boyer, K.L., Kak, A.C.: Color-encoded structured light for rapid active ranging. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 14–28 (1987)CrossRefGoogle Scholar
- 10.Durdle, N.G., Thayyoor, J., Raso, V.J.: An improved structured light technique for surface reconstruction of the human trunk. In: IEEE Canadian Conference on Electrical and Computer Engineering, vol. 2, pp. 874–877 (1998)Google Scholar
- 11.Petriu, E.M., Sakr, Z., Spoelder, H.J.W., Moica, A.: Object recognition using pseudo-random color encoded structured light. In: Conference Record - IEEE Instrumentation and Measurement Technology Conference, vol. 3, pp. 1237–1241 (2000)Google Scholar
- 12.Dal Mutto, C., Zanuttigh, P., Cortelazzo, G.M.: Time-of-Flight Cameras and Microsoft Kinect\(^{\rm TM}\), 1st edn. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4614-3807-6Google Scholar
- 13.Webster, D., Celik, O.: Experimental evaluation of Microsoft Kinect’s accuracy and capture rate for stroke re-habilitation applications. In: Proceedings of Haptics Symposium, no. 1, pp. 455–460. IEEE (2014)Google Scholar
- 14.Nagayama, R., Kazuma, T., Endo, T., He, A.: A basic study of human face direction estimation using depth sensor. In: Proceedings of International Joint Conference on Awareness Science and Technology and Ubi-Media Computing, pp. 644–648 (2013)Google Scholar
- 15.Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19, 4–10 (2012)CrossRefGoogle Scholar
- 16.Kawasue, K., Ikeda, T., Tokunaga, T., Harada, H.: Three-dimensional shape measurement system for black cattle using kinect sensor. Int. J. Circuit Signal Process. 7(4), 220–230 (2013)Google Scholar
- 17.Kawasue, K., Win, K.D., Yoshida, K., Tokunaga, T.: Black cattle body shape and temprature measurement using themography and kinect sensor. Artif. Life Robot. 22(4), 464–470 (2017)CrossRefGoogle Scholar
- 18.Guo, H., et al.: 3D scanning of live pigs system and its application in body measurements. Proc. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 42, 211–217 (2017)CrossRefGoogle Scholar
- 19.Zhang, Z.: Determining the epipolar geometry and its uncertainty. IJCV 27(2), 161–198 (1998)CrossRefGoogle Scholar
- 20.Chai, J., Ma, S.: Robust epipolar geometry estimation using genetic algorithm. PRL 19(9), 829–838 (1998)CrossRefGoogle Scholar
- 21.Torras, C.: Computer Vision: Theory and Industrial Applications. Springer, Heidelberg (1992). https://doi.org/10.1007/978-3-642-48675-3CrossRefGoogle Scholar