Abstract
Livestock farming industries, as well as almost any industry, want more and more data about the operation of their business and activities in order to make the right decisions. However, especially when considering very large animal farms, the precise and up-to-date information about the position and numbers of the animals is rather difficult to obtain. In this contribution, a novel engineering approach to livestock positioning and counting, based on image processing, is proposed. The approach is composed of two parts. Namely, a fully convolutional neural network for input image transformation, and a locator for animal positioning. The transformation process is designed in order to transform the original RGB image into a gray-scale image, where animal positions are highlighted as gradient circles. The locator then detects the positions of the circles in order to provide the positions of animals. The presented approach provides a precision rate of 0.9842 and a recall rate of 0.9911 with the testing set, which is, in combination with a rather suitable computational complexity, a good premise for the future implementation under real conditions.
The work has been supported by SGS grant at Faculty of Electrical Engineering and Informatics, University of Pardubice, Czech Republic. This support is very gratefully acknowledged.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arnal Barbedo, J.G., Koenigkan, L.V.: Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook Agr. 47(3), 214–222 (2018). https://doi.org/10.1177/0030727018781876
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017). https://doi.org/10.1109/TPAMI.2016.2644615
Bhakta, I., Phadikar, S., Majumder, K.: State-of-the-art technologies in precision agriculture: a systematic review. J. Sci. Food Agric. 99(11), 4878–4888 (2019). https://doi.org/10.1002/jsfa.9693
Bishop, J.C., Falzon, G., Trotter, M., Kwan, P., Meek, P.D.: Livestock vocalisation classification in farm soundscapes. Comput. Electron. Agric. 162, 531–542 (2019). https://doi.org/10.1016/j.compag.2019.04.020
Cowlar: Streamline your dairy business! (2020). https://www.cowlar.com/
Dhulekar, P.A., Gandhe, S.T., Bagad, G.R., Dwivedi, S.S.: Vision based technique for animal detection. In: 2018 International Conference on Advances in Communication and Computing Technology (ICACCT), pp. 344–348, February 2018. https://doi.org/10.1109/ICACCT.2018.8529660
Ismail, Z.H., Chun, A.K.K., Shapiai Razak, M.I.: Efficient herd – outlier detection in livestock monitoring system based on density – based spatial clustering. IEEE Access 7, 175062–175070 (2019). https://doi.org/10.1109/ACCESS.2019.2952912
Kellenberger, B., Marcos, D., Courty, N., Tuia, D.: Detecting animals in repeated UAV image acquisitions by matching CNN activations with optimal transport. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 3643–3646, July 2018. https://doi.org/10.1109/IGARSS.2018.8519012
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
Li, X., Xing, L.: Use of unmanned aerial vehicles for livestock monitoring based on streaming K-means clustering. IFAC PapersOnLine 52(30), 324–329 (2019). https://doi.org/10.1016/j.ifacol.2019.12.560
McKinlay, J., Southwell, C., Trebilco, R.: Integrating count effort by seasonally correcting animal population estimates (ICESCAPE): a method for estimating abundance and its uncertainty from count data using Adelie penguins as a case study. CCAMLR Sci. 17, 213–227 (2010)
Nyamuryekung’e, S., Cibils, A.F., Estell, R.E., Gonzalez, A.L.: Use of an unmanned aerial vehicle-mounted video camera to assess feeding behavior of Raramuri Criollo cows. Rangeland Ecol. Manag. 69(5), 386–389 (2016). https://doi.org/10.1016/j.rama.2016.04.005
Parikh, M., Patel, M., Bhatt, D.: Animal detection using template matching algorithm. Int. J. Res. Mod. Eng. Emerg. Technol. 1(3), 26–32 (2013)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015). http://arxiv.org/abs/1505.04597
Sarwar, F., Griffin, A., Periasamy, P., Portas, K., Law, J.: Detecting and counting sheep with a convolutional neural network. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6, November 2018. https://doi.org/10.1109/AVSS.2018.8639306
Seo, J., Sa, J., Choi, Y., Chung, Y., Park, D., Kim, H.: A yolo-based separation of touching-pigs for smart pig farm applications. In: 2019 21st International Conference on Advanced Communication Technology (ICACT), pp. 395–401, February 2019. https://doi.org/10.23919/ICACT.2019.8701968
Sharma, P., Singh, A.: Era of deep neural networks: a review. In: 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017 (2017). https://doi.org/10.1109/ICCCNT.2017.8203938
Xu, Y., Zhou, X., Chen, S., Li, F.: Deep learning for multiple object tracking: a survey. IET Comput. Vision 13(4), 355–368 (2019). https://doi.org/10.1049/iet-cvi.2018.5598
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dolezel, P. et al. (2021). Counting Livestock with Image Segmentation Neural Network. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-57802-2_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57801-5
Online ISBN: 978-3-030-57802-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)