Recognition of Hereford and Simmental Cattle Breeds via Computer Vision

Subject Areas : Camel

M.I. Yeşil ¹ , S. Göncü ²

1 - Department of Animal Science, Faculty of Agriculture, Çukurova University, Adana, Türkiye
2 - Department of Animal Science, Faculty of Agriculture, Çukurova University, Adana, Türkiye

Received: 2022-07-22 Accepted : 2022-10-15 Published : 2023-03-01

Keywords: Neural network, Image recognition, Artificial Intelligence, cattle breed classification, computer vision, Hereford and Simmental,

Abstract :

Self-sufficient unmanned systems like computer vision products increasingly become essential in managing the data and supporting producers in making decisions in the livestock environment. Image classification is one of the most famous missions for machine learning (ML) methods and its goal is to detect the objects in the frames. The main objective of the research was to investigate whether the classification of two breeds, Hereford and Simmental, often confused with each other due to their morphological similarities, via image processing, is helpful in the case of livestock production. 600 images of different individuals from Hereford (300) and Simental (300) cattle breeds were included in the study. The Fully Connected Neural Networks (FCNN) were established estimate the breeds. Modelling of artificial neural networks, image processing and all other analyses was conducted with EBimage and Keras packages in R language on a PC with 11. Gen. i7 CPU and CUDA supported GPU model of RTX 3060. The results show only 17 were inaccurate in 600 images in total with an accuracy greater than 97%. The training process of the model was executed in 69 seconds. At the end of the investigation, it was clarified that the use of FCNN in livestock would be beneficial in terms of breed classification via image recognition.

References:

Asadi B. and H. Jiang (2020). On Approximation Capabilities of ReLU Activation and Softmax Output Layer in Neural Networks. arXiv preprint arXiv. 2002, 40-60.

Bello R.W., Talib A.Z., Mohamed A.S.A., Olubummo D.A. and Otobo F.N. (2020). Image-based individual cow recognition using body patterns. Int. J. Adv. Comput. Sci. Appl. 11(3), 92-98.

Bene S., Nagy B., Nagy L., Kiss B., Polgár J.P. and Szabó F. (2007). Comparison of body measurements of beef cows of different breeds. Arch. Anim. Breed. 50(4), 363-373.

Billah M., Wang X., Yu J. and Jiang Y. (2022). Real-time goat face recognition using convolutional neural network. Comput. Electron. Agric. 194, 15-24.

Borges O.D.A., Ribeiro Pereira L.G., Bresolin T., Pontes Ferreira R.E. and Reboucas Dorea J.R. (2021). A review of deep learning algorithms for computer vision systems in livestock. Livest. Sci. 253, 1-46.

Bowling M.B., Pendell D.L., Morris D.L., Yoon Y., Katoh K., Belk K.E. and Smith G.C. (2008). Review: Identification and traceability of cattle in selected countries outside of North America. Prof. Anim. Sci. 24(4), 287-294.

Cevik K.K. and Boga M. (2019). Body condition score (BCS) classification with deep learning. Pp. 24-27 in Proc. Innov. Intell. Syst. Appl. Conf., İzmir, Turkey.

Da Costa G.B.P., Contato W.A., Nazare T.S., Neto J.E. and Ponti M. (2016). An empirical study on the effects of different types of noise in image classification tasks. arXiv preprint arXiv. 2016, 1-6.

Dutta A. Veldhuis R. and Spreeuwers L. (2012). The impact of image quality on the performance of face recognition. arXiv preprint arXiv. 2012, 141-148.

Ensminger M.E. (1990). Animal Science. Interstate Publishers Inc., Danville, İllinois, USA.

Felius M., Koolmees P.A., Theunissen B. and Lenstra J.A. (2011). On the breeds of cattle-historic and current classifications. Diversity. 3(4), 660-692.

Göncü S. (2020). Sığırcılık. Akademisyen Kitabevi, Ankara, Turkey. (in Turkish).

Guyon I. (1997). A scaling law for the validation-set training-set size ratio. AT&T Bell Lab. 1997, 1-11.

Haider S.A., Naqvi S.R., Akram T., Umar G.A., Shahzad A., Rafiq Sial M., Khaliq S. and Kamran M. (2019). LSTM neural network-based forecasting model for wheat production in Pakistan. Agronomy. 9(2), 72-84.

Inik O. and Ülker E. (2017). Deep Learning and Deep Learning Models Used in Image Analysis 2017; Gaziosmanpasa J. Sci. Res. 6(3), 85-104.

Koziarski M. and Cyganek B. (2017). Image recognition with deep neural networks in presence of noise Dealing with and taking advantage of distortions. Integr. Comput. Aided Engin. 24(4), 337-349.

Kumar S., Singh S.K., Singh R. and Singh A.K. (2017). Recognition of cattle using face images. Anim. Biometrics. 2017, 79-110.

Liu S. and Deng W. (2015). Very deep convolutional neural network-based image classification using small training sample size. Pp. 730-734 in Proc. 3^rd IAPR Asian Conf. Pattern Recog. ACPR. Kuala Lumpur, Malaysia.

Martins B.M., Mendes A.L.C., Silva L.F., Moreira T.R., Costa J.H.C., Rotta P.P., Chizzotti M.L. and Marcondes M.I. (2020). Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livest. Sci. 236, 1-19.

Massouh N., Babiloni F., Tommasi T., Young J., Hawes N. and Caputo B. (2017). Learning deep visual object models from noisy web data: How to make it work. Pp. 5564-5571 in IEEE/RSJ Int. Conf. Intellig. Robots Syst., Vancouver, Canada.

Okura F., Ikuma S., Makihara Y., Muramatsu D., Nakada, K. and Ygi Y. (2019). RGB-D video-based individual identification of dairy cows using gait and texture analyses. Comput. Electron. Agric. 165, 1-15.

Özkütük K. and Şekerden Ö. (1990). Büyükbaş Hayvan Yetiştirme. Çukurova Üniversitesi Ziraat Fakültesi, Adana, Turkey. (In Turkish)

Pau G., Fuchs F., Sklyar O., Boutros M. and Huber W. (2010). EBImage-an R package for image processing with applications to cellular phenotypes. Bioinformatics. 26(7), 979-981.

Piotrowski A.P. and Napiorkowski J.J. (2013). A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling. J. Hydrol. 476, 97-111.

Qiao Y., Kong H., Clark C., Lomax S., Su, D., Eiffert S. and Sukkarieh S. (2021). Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation. Comput. Electron. Agric. 185, 1-14.

Qiao Y., Su D., Kong H., Sukkarieh S., Lomax S. and Clark C. (2019). Individual cattle identification using a deep learning based framework. IFAC-PapersOnLine. 52(30), 318-323.

Salau J., Haas J.H., Junge W., Bauer U., Harms J. and Bieletzki S. (2014). Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns. Biomed. Life Sci. 3(1), 225-239.

Sokolova M. and Lapalme G. (2009). A systematic analysis of performance measures for classification tasks. Inf. Proc. Manage. 45(4), 427-437.

Soydaner D. (2020). A comparison of optimization algorithms for deep learning. Int. J. Pattern Recognit. Art. Intellig. 34(13), 1-10.

Too E.C., Yujian L., Njuki S. and Yingchun L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272-279.

Türkiye İstatistik Kurumu. (2021). Hayvansal Üretim İstatistikleri Tablolar, Türkiye.

Weber F.L., Weber V.A. M., Menezes G.V., Oliveira Junior A.S., Alves, D.A., Morais de Oliveira M.V., Matsubara E.T., PistorI H. and Pinto de Abreu U.G. (2020). Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks. Comput. Electron. Agric. 175, 1-13.

Wu D., Wu Q., Yin X., Jiang B., Wang H., He D. and Song H. (2020). Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector. Biosys. Eng. 189, 150-163.

Zhang Y., Gao J. and Zhou H. (2020). Breeds classification with deep convolutional neural network. Pp. 145-151 in ACM Int. Conf. Proc., Shenzhen, China.

Zhao K. and He D. (2013). Recognition of individual dairy cattle based on convolutional neural networks. Trans. Chinese Soc. Agric. Eng. 31, 181-187.