Inferring Broiler Chicken Weight through Machine Learning
Resumo
Este trabalho apresenta uma metodologia para pesagem de aves de corte através de análise de imagens. Nosso método consiste na obtenção de imagens a uma altura fixa do solo, com o processamento ocorrendo em três etapas. Primeiro, utiliza-se uma rede neural para classificação, capaz de determinar o contorno das aves através da aplicação elipses, resultado em imagens binárias. Segundo, aplica-se um método para extração de características geométricas a partir das imagens binárias geradas. Por último, utiliza-se uma rede neural para inferência de peso a partir das características geométricas. Como resultado, nossa técnica permite a inferência do peso das aves in seu local de crescimento. O erro médio de predição de peso foi de 5.34%.
Palavras-chave:
aprendizado de máquina, inferência de peso, aves de corte
Referências
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Dohmen, R., Catal, C., and Liu, Q. (2021). Image-based body mass prediction of heifers using deep neural networks. Biosystems Engineering, 204:283–293.
Dutta, A. and Zisserman, A. (2019). The VIA annotation software for images, audio and video. In Proceedings of the 27th ACM International Conference on Multimedia, MM ’19, New York, NY, USA. ACM.
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He, K., Gkioxari, G., Doll´ar, P., and Girshick, R. (2017). Mask r-cnn. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2980–2988.
Jierula, A., Wang, S., Oh, T.-M., and Wang, P. (2021). Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data. Applied Sciences, 11:2314.
Kaloev, M. and Krastev, G. (2021). Comparative analysis of activation functions used in the hidden layers of deep neural networks. In 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pages 1–5.
Kwok, T.-Y. and Yeung, D.-Y. (1995). Efficient cross-validation for feedforward neural networks. In Proceedings of ICNN’95 - International Conference on Neural Networks, volume 5, pages 2789–2794 vol.5.
Meshram, V., Patil, K., Meshram, V., Hanchate, D., and Ramkteke, S. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1:100010.
Müller, A. and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Incorporated.
Raju, V. N. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., and Padma, V. (2020). Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pages 729–735.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. J. Big Data, 6:60.
Somaye Amraei, Saman A. Mehdizadeh, I. d. A. N. (2018). Development of a transfer function for weight prediction of live broiler chicken using machine vision. Revista Engenharia Agrícola, 38:776–782.
Souza, P., Fornazier, A., Souza, H., and Ponciano, N. (2019). Diferenças regionais de tecnologia na agricultura familiar no brasil. Revista de Economia e Sociologia Rural, 57:594–617.
Tolentino, L. K., Pedro, C., Icamina, J., Navarro, J., Salvacion, L., Sobrevilla, G., Villanueva, A., Amado, T., Padilla, M. V., and Madrigal, G. (2020). Weight prediction system for nile tilapia using image processing and predictive analysis. International Journal of Advanced Computer Science and Applications, 11:399–406.
Beitzel, S. M., Jensen, E. C., and Frieder, O. (2009). MAP, pages 1691–1692. Springer US, Boston, MA.
Cepea (2022). Pib do agronegócio. Indicadores, CEPEA.
Charu, C. A. (2018). Neural networks and deep learning: a textbook.
Dohmen, R., Catal, C., and Liu, Q. (2021). Image-based body mass prediction of heifers using deep neural networks. Biosystems Engineering, 204:283–293.
Dutta, A. and Zisserman, A. (2019). The VIA annotation software for images, audio and video. In Proceedings of the 27th ACM International Conference on Multimedia, MM ’19, New York, NY, USA. ACM.
Fine, T. L., Lauritzen, S. L., Jordan, M., Lawless, J., and Nair, V. (1999). Feedforward Neural Network Methodology. Springer-Verlag, Berlin, Heidelberg, 1st edition.
He, K., Gkioxari, G., Doll´ar, P., and Girshick, R. (2017). Mask r-cnn. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2980–2988.
Jierula, A., Wang, S., Oh, T.-M., and Wang, P. (2021). Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data. Applied Sciences, 11:2314.
Kaloev, M. and Krastev, G. (2021). Comparative analysis of activation functions used in the hidden layers of deep neural networks. In 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pages 1–5.
Kwok, T.-Y. and Yeung, D.-Y. (1995). Efficient cross-validation for feedforward neural networks. In Proceedings of ICNN’95 - International Conference on Neural Networks, volume 5, pages 2789–2794 vol.5.
Meshram, V., Patil, K., Meshram, V., Hanchate, D., and Ramkteke, S. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1:100010.
Müller, A. and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Incorporated.
Raju, V. N. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., and Padma, V. (2020). Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pages 729–735.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. J. Big Data, 6:60.
Somaye Amraei, Saman A. Mehdizadeh, I. d. A. N. (2018). Development of a transfer function for weight prediction of live broiler chicken using machine vision. Revista Engenharia Agrícola, 38:776–782.
Souza, P., Fornazier, A., Souza, H., and Ponciano, N. (2019). Diferenças regionais de tecnologia na agricultura familiar no brasil. Revista de Economia e Sociologia Rural, 57:594–617.
Tolentino, L. K., Pedro, C., Icamina, J., Navarro, J., Salvacion, L., Sobrevilla, G., Villanueva, A., Amado, T., Padilla, M. V., and Madrigal, G. (2020). Weight prediction system for nile tilapia using image processing and predictive analysis. International Journal of Advanced Computer Science and Applications, 11:399–406.
Publicado
25/09/2023
Como Citar
SOUSA, Lucas H. N. de; FERRANDIN, Mauri; MORATELLI, Carlos.
Inferring Broiler Chicken Weight through Machine Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 374-388.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234179.
