RUMICAM: A New Device for Cattle Rumination Analysis
Resumo
Rumination may reveal important behavioral aspects of livestock animals and has been increasingly studied using new sensors technologies. In this work a new device was developed to collect close-up videos from the animal mouth during the rumination period. Using shallow and deep machine learning techniques, a software that classifies the basic mouth movements from these images has also been developed. A baseline performance for this equipment has been established using the Fscore metric. SVM achieved the highest F-score of 79.3% for the shallow learning approach. The best F-score using deep learning was 75% using VGG16.
Referências
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E. Cáceres, H. Pistori, M. Turine, P. Pires, C. Soares, and C. Carromeu, "Computational livestock precision position paper," in Second Work- shop of the Brazilian Institute for Web Science Research, 2011.
S. Neethirajan, S. K. Tuteja, S.-T. Huang, and D. Kelton, "Recent advancement in biosensors technology for animal and livestock health management," Biosensors and Bioelectronics, vol. 98, pp. 398 – 407, 2017.
D. Lovarelli, J. Bacenetti, and M. Guarino, "A review on dairy cattle farming: Is precision livestock farming the compromise for an envi- ronmental, economic and social sustainable production?" Journal of Cleaner Production, vol. 262, p. 121409, 2020.
G. Marchesini, D. Mottaran, B. Contiero, E. Schiavon, S. Segato, E. Garbin, S. Tenti, and I. Andrighetto, "Use of rumination and activity data as health status and performance indicators in beef cattle during the early fattening period," The Veterinary Journal, vol. 231, pp. 41 – 47, 2018.
U. Braun, S. Zürcher, and M. Hässig, "Evaluation of eating and rumination behaviour in 300 cows of three different breeds using a noseband pressure sensor," BMC veterinary research, vol. 11, no. 09, p. 231, 2015.
N. Zehner, C. Umstätter, J. J. Niederhauser, and M. Schick, "System specication and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows," Computers and Electronics in Agriculture, vol. 136, pp. 31 – 41, 2017.
N. Ali, D. Neagu, and P. Trundle, "Evaluation of k-nearest neighbour classier performance for heterogeneous data sets," SN Applied Sciences, vol. 1, no. 1559, 2019.
J. Nalepa and M. Kawulok, "Selecting training sets for support vector machines: a review," Articial Intelligence Review, vol. 52, pp. 857 – 900, 2019.
J. A. Wyner, M. Olson, J. Bleich, and D. Mease, "Explaining the success of adaboost and random forests as interpolating classiers," Journal of Machine Learning Research, vol. 18, 2017.
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in International Conference on Learning Representations, 2015.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in 2016 IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826.
F. Chollet, "Xception: Deep learning with depthwise separable convo- lutions," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1800–1807.
C. Bouvier, A. Benoit, A. Caplier, and P.-Y. Coulon, "Open or closed mouth state detection: Static supervised classication based on log- polar signature," in Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, vol. 5259, 10 2008.
C. Souto Maior, M. Moura, J. Santana, L. Nascimento, J. Macedo, I. Lins, and E. Droguett, "Real-time svm classication for drowsiness detection using eye aspect ratio," in Proceedings of Probabilistic Safety Assessment and Management PSAM, 09 2018.
J. G. A. Barbedo, "Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classication," Compute rs and Electronics in Agriculture, vol. 153, pp. 46 – 53, 2018.
Publicado
07/10/2020
Como Citar
DE OLIVEIRA, Gilberto et al.
RUMICAM: A New Device for Cattle Rumination Analysis. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 93-97.
DOI: https://doi.org/10.5753/wvc.2020.13487.
