Abstract
Using a balance to estimate sheep’s weight is inefficient and time consuming. Sheep’s weight also fluctuates with many factors such as pregnancy, lactation, and gut fill. However, linear measurements are not highly affected by such type of factors. Therefore, in this paper, sheep weight was determined by calculating linear measurements from sheep images using visual analysis techniques. The system starts, followed by applying the K-means clustering for sheep segmentation. Then, biggest blob detection along with morphological analysis take place. After that breadth and width of sheep are extracted. Weight is then estimated from the linear dimensions using a regression function learned from the data-set. In the experiments, sheep weight estimation was tested on data set of 104 side images for 52 sheep. For performance evaluation, R-squared was measured and it reached 0.99. High accuracy of 98.75% was also achieved.
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References
Burke, J., Nuthall, P., McKinnon, A.: An Analysis of the Feasibility of Using Image Processing to Estimate the Live Weight of Sheep. Farm and Horticultural Management Group Applied Management and Computing Division, Lincoln University (2004). http://hdl.handle.net
Begaz, S., Awgichew, K.: Estimation of weight and age of sheep and goats. Ethiopia Sheep and Goat Productivity Improvement Program (ESGPIP). Ethiopia, No. 23 (2009). http://www.esgpip.org/PDF/Technical%20bulletin%20No.23.pdf
Chen, W., Wang, C.: The human-height measurement scheme using image processing techniques. Int. J. Comput. Consum. Control (IJ3C) 4(3), 186–189 (2015)
Menesatti, P., Costa, C., Antonucci, F., Steri, R., Pallottino, F., Catillo, G.: A low-cost stereovision system to estimate size and weight of live sheep. Comput. Electron. Agric. 103, 33–38 (2014)
Kashiha, H., et al.: Automatic weight estimation of individual pigs using image analysis. Comput. Electron. Agric. 107, 38–44 (2014)
Pradana, Z., Hidayat, B., Darana, S.: Beef Cattle Weight Determine by Using Digital Image Processing. Control Electronics (2016). https://ieeexplore.ieee.org/document/7814955/
Khojastehkey, M., Aslaminejad, A.A., Shariati, M.M., et al.: Body size estimation of new born lambs using image processing and its effect on the genetic gain of a simulated population. J. Appl. Anim. Res. 44(1), 326–330 (2016)
Chen, K.: K-means Clustering. COMP24111 Machine Learning Course (2016). https://studentnet.cs.manchester.ac.uk/ugt/COMP24111/
Gogtay, N., Deshpande, S., Thatte, U.: Principles of regression analysis. J. Assoc. Physicians India 65, 48 (2017). http://www.japi.org/april_2017/08_sfr_principles_of_regression_analysis.html
Mouhaffel, A.G., Domìnguez, C.M., Arcones, B., Redonda, F.M., Martín, R.D.: Using multiple regression analysis lineal to predict occupation market work in occupational hazard prevention services. Int. J. Appl. Eng. Res. 12(3), 283–288 (2017)
Hassan, R., Rahman Ema, R., Islam, T.: Color image segmentation using automated K-means clustering with RGB and HSV color spaces. Glob. J. Comput. Sci. Technol. 17(3), 0975–4350 (2017)
Jaikla, C., Rasmequan, S.: Segmentation of optic disc and cup in fundus images using maximally stable extremal regions. In: International Workshop on Advanced Image Technology 2018 (IWAIT 2018), 7–9 January 2018, Chiang Mai, Thailand. IEEE (2018)
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Abdelhady, A.S., Hassanien, A.E., Awad, Y.M., El-Gayar, M., Fahmy, A. (2019). Automatic Sheep Weight Estimation Based on K-Means Clustering and Multiple Linear Regression. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_50
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DOI: https://doi.org/10.1007/978-3-319-99010-1_50
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