Inferring Broiler Chicken Weight through Machine Learning
Abstract
This study presents a methodology for weighing broiler chickens using image analysis. Our method involves capturing images at a fixed height above the ground, with the processing occurring in three stages. First, a neural network is employed for classification, capable of determining the outline of the birds by applying ellipses, resulting in binary images. Second, a method is applied to extract geometric features from the generated binary images. Finally, another neural network is used to infer weight based on the geometric features. As a result, our technique allows for the inference of the bird’s weight at their growing site. The average weight prediction error was 5.34%.
Keywords:
machine learning, weight inference, broiler chicken
References
AVILA, V. S. D., FERREIRA, J. F. R., PIENIZ, L. C., LEDUR, M. C., ALBINO, L. F. T., and OLIVEIRA, P. A. V. D. (2007). Boas práticas de produção de frangos de corte. Circular interna, EMBRAPA.
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.
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.
Published
2023-09-25
How to Cite
SOUSA, Lucas H. N. de; FERRANDIN, Mauri; MORATELLI, Carlos.
Inferring Broiler Chicken Weight through Machine Learning. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (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.
