Breed recognition and estimation of live weight of cattle based on methods of machine learning and computer vision

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.247648

Keywords:

image processing, convolutional network, multilayer perceptron, stereopsis, predictive model

Abstract

A method of measuring cattle parameters using neural network methods of image processing was proposed. To this end, several neural network models were used: a convolutional artificial neural network and a multilayer perceptron. The first is used to recognize a cow in a photograph and identify its breed followed by determining its body dimensions using the stereopsis method. The perceptron was used to estimate the cow's weight based on its breed and size information. Mask RCNN (Mask Regions with CNNs) convolutional network was chosen as an artificial neural network.

To clarify information on the physical parameters of animals, a 3D camera (Intel RealSense D435i) was used. Images of cows taken from different angles were used to determine the parameters of their bodies using the photogrammetric method.

The cow body dimensions were determined by analyzing animal images taken with synchronized cameras from different angles. First, a cow was identified in the photograph and its breed was determined using the Mask RCNN convolutional neural network. Next, the animal parameters were determined using the stereopsis method. The resulting breed and size data were fed to a predictive model to determine the estimated weight of the animal.

When modeling, Ayrshire, Holstein, Jersey, Krasnaya Stepnaya breeds were considered as cow breeds to be recognized. The use of a pre-trained network with its subsequent training applying the SGD algorithm and Nvidia GeForce 2080 video card has made it possible to significantly speed up the learning process compared to training in a CPU.

The results obtained confirm the effectiveness of the proposed method in solving practical problems.

Author Biographies

Oleksandr Bezsonov, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor

Department of Сomputer Intelligent Technologies and Systems

Oleh Lebediev, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Electronic Computers

Valentyn Lebediev, Kharkiv National University of Radio Electronics

Postgraduate Student

Department of Electronic Computers

Yuriy Megel, Kharkiv Petro Vasylenko National Technical University of Agriculture

Doctor of Technical Sciences, Professor, Head of Department

Department of Cybernetics

Dmytro Prochukhan, Kharkiv Computer Technology Professional College of the National Technical University «Kharkiv Polytechnic Institute»

Lecturer

Oleg Rudenko, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor, Head of Department

Department of Сomputer Intelligent Technologies and Systems

References

  1. Tasdemir, S., Urkmez, A., Inal, S. (2011). Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Computers and Electronics in Agriculture, 76 (2), 189–197. doi: https://doi.org/10.1016/j.compag.2011.02.001
  2. Celik, S., Eyduran, E., Karadas, K., Tariq, M. M. (2017). Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan. Revista Brasileira de Zootecnia, 46 (11), 863–872. doi: https://doi.org/10.1590/s1806-92902017001100005
  3. McNitt, J. I. (1983). Livestock Husbandry Techniques. London: Granada publishing company limited, 288.
  4. Adamczyk, K., Molenda, K., Szarek, J., Skrzyński, G. (2005). Prediction of Bulls’slaughter Value From Growth Data Using Artificial Neural Network. Journal of Central European Agriculture, 6 (2), 133–142. Available at: https://www.academia.edu/22135262/Prediction_of_BullsSlaughter_Value_From_Growth_Data_Using_Artificial_Neural_Network_Przewidywanie_Warto%C5%9Bci_Rze%C5%BAnej_
  5. Akkol, S., Akilli, A., Cemal, İ. (2017). Comparison of Artificial Neural Network and Multiple Linear Regression for Prediction of Live Weight in Hair Goats. Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi, 27 (1), 21–29. doi: https://doi.org/10.29133/yyutbd.263968
  6. Khorshidi‐Jalali, M., Mohammadabadi, M. R., Esmailizadeh, A., Barazandeh, A., Babenko, O. I. (2019). Comparison of Artificial Neural Network and Regression Models for Prediction of Body Weight in Raini Cashmere Goat. Iranian Journal of Applied Animal Science, 9 (3), 453–461. Available at: http://ijas.iaurasht.ac.ir/article_667543_108967f61ea69bafe983318670f81ffd.pdf
  7. Nicolas, F. F. C., Saludes, R. B., Relativo, P. L. P., Saludes, T. A. (2018). Estimating live weight of philippine dairy buffaloes (bubalus bubalis) using digital image analysis. Philipp J Vet Anim Sci, 44 (2), 129–138. Available at: https://www.pjvas.org/index.php/pjvas/article/view/207/183
  8. Shahinfar, S., Mehrabani-Yeganeh, H., Lucas, C., Kalhor, A., Kazemian, M., Weigel, K. A. (2012). Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems. Computational and Mathematical Methods in Medicine, 2012, 1–9. doi: https://doi.org/10.1155/2012/127130
  9. Ali, M., Eyduran, E., Tariq, M. M., Tirink, C., Abbas, F., Bajwa, M. A. et. al. (2015). Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pakistan J. Zool., 47 (6), 1579–1585. Available at: http://zsp.com.pk/pdf47/1579-1585%20(10)%20QPJZ-0146-2015%2014-7-15%20REVISEDVERSION_FINAL.pdf
  10. Mortensen, A. K., Lisouski, P., Ahrendt, P. (2016). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 123, 319–326. doi: https://doi.org/10.1016/j.compag.2016.03.011
  11. Raja, T. V., Ruhil, A. P., Gandhi, R. S. (2011). Comparison of connectionist and multiple regression approaches for prediction of body weight of goats. Neural Computing and Applications, 21 (1), 119–124. doi: https://doi.org/10.1007/s00521-011-0637-z
  12. Salawu, E. O., Abdulraheem, M., Shoyombo, A., Adepeju, A., Davies, S., Akinsola, O., Nwagu, B. (2014). Using Artificial Neural Network to Predict Body Weights of Rabbits. Open Journal of Animal Sciences, 04 (04), 182–186. doi: https://doi.org/10.4236/ojas.2014.44023
  13. Szyndler-Nędza, M., Eckert, R., Blicharski, T., Tyra, M., Prokowski, A. (2016). Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks. Annals of Animal Science, 16 (1), 275–286. doi: https://doi.org/10.1515/aoas-2015-0057
  14. Wang, Y., Yang, W., Winter, P., Walker, L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering, 100 (1), 117–125. doi: https://doi.org/10.1016/j.biosystemseng.2007.08.008
  15. Wu, J., Tillett, R., McFarlane, N., Ju, X., Siebert, J. P., Schofield, P. (2004). Extracting the three-dimensional shape of live pigs using stereo photogrammetry. Computers and Electronics in Agriculture, 44 (3), 203–222. doi: https://doi.org/10.1016/j.compag.2004.05.003
  16. Wongsriworaphon, A., Arnonkijpanich, B., Pathumnakul, S. (2015). An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, 115, 26–33. doi: https://doi.org/10.1016/j.compag.2015.05.004
  17. Yilmaz, H. M., Yakar, M., Yildiz, F. (2008). Digital Photogrammetry in Obtaining of 3D Model Data of Irregular Small Objects. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Beijing, 125–130. Available at: https://www.isprs.org/proceedings/XXXVII/congress/3b_pdf/23.pdf
  18. Radwan, H., Qaliouby, H., Elfadl, E. (2020). Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models. Journal of Advanced Veterinary and Animal Research, 7 (3), 429. doi: https://doi.org/10.5455/javar.2020.g438
  19. Tasdemir, S., Ozkan, I. A. (2019). Ann approach for estimation of cow weight depending on photogrammetric body dimensions. International Journal of Engineering and Geosciences. doi: https://doi.org/10.26833/ijeg.427531
  20. Cooper, M. A. R., Robson, S. (1996). Theory of Close Range Photogrammetry. Close Range Photogrammetry and Machine Vision, 9–51.
  21. Yilmaz, H. M. (2010). Close range photogrammetry in volume computing. Experimental Techniques, 34 (1), 48–54. doi: https://doi.org/10.1111/j.1747-1567.2009.00476.x
  22. Yakar, M., Yilmaz, H. (2008). Using in volume computing of digital close range photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Beijing, 119–124. Available at: https://www.isprs.org/proceedings/xxxvii/congress/3b_pdf/22.pdf
  23. Hartley, R., Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press. doi: https://doi.org/10.1017/cbo9780511811685
  24. Bradski, G., Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, 580.
  25. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., Jackel, L. D. (1989). Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1 (4), 541–551. doi: https://doi.org/10.1162/neco.1989.1.4.541
  26. LeCun, Y., Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, 255–258.
  27. Girshick, R. (2015). Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV). doi: https://doi.org/10.1109/iccv.2015.169
  28. Wang, J., Ye, Z. (2018). An improved faster R-CNN approach for robust hand detection and classification in sign language. Tenth International Conference on Digital Image Processing (ICDIP 2018). doi: https://doi.org/10.1117/12.2503080
  29. He, K., Gkioxari, G., Dollar, P., Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). doi: https://doi.org/10.1109/iccv.2017.322
  30. Nair, V., Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27 th International Conference on Machine Learning.
  31. Zhang, Z. (2016). Derivation of Backpropagation in Convolutional Neural Network (CNN). Available at: https://zzutk.github.io/docs/reports/2016.10%20-%20Derivation%20of%20Backpropagation%20in%20Convolutional%20Neural%20Network%20(CNN).pdf
  32. Raimi, B. K. (2015). 10 Gradient Descent Optimisation Algorithms + Cheat Sheet. Available at: https://www.kdnuggets.com/2019/06/gradient-descent-algorithms-cheat-sheet.html
  33. Rudenko, O., Bezsonov, O., Oliinyk, K. (2020). First-Order Optimization (Training) Algorithms in Deep Learning. Proceedings of the 4th International Conference on Computational Linguistics and Intelligent Systems (COLINS 2020). Volume I: Main Conference. Lviv, 921–935. Available at: http://ceur-ws.org/Vol-2604/paper61.pdf
  34. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. Available at: https://github.com/matterport/Mask_RCNN
  35. Image augmentation for machine learning experiments. Available at: https://github.com/aleju/imgaug

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Published

2021-12-29

How to Cite

Bezsonov, O., Lebediev, O., Lebediev, V., Megel, Y., Prochukhan, D., & Rudenko, O. (2021). Breed recognition and estimation of live weight of cattle based on methods of machine learning and computer vision. Eastern-European Journal of Enterprise Technologies, 6(9 (114), 64–74. https://doi.org/10.15587/1729-4061.2021.247648

Issue

Vol. 6 No. 9 (114) (2021): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2021 Oleksandr Bezsonov, Oleh Lebediev, Valentyn Lebediev, Yuriy Megel, Dmytro Prochukhan, Oleg Rudenko

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