Machine learning aplicado al análisis de un set de datos de parámetros ambientales en galpones de pollos de engorde

Gloris Batista-Mendoza; Edwin Juvenal Cedeño Herrera; Gloris Cedeño-Batista

doi:10.48204/j.vian.v7n2.a4566

Submitted December 29, 2023

Published 2023-12-29

Artículos

Vol. 7 No. 2 (2023): Visión Antataura

Machine learning applied to the analysis of a dataset of environmental parameters in poultry farm

Gloris Batista-Mendoza⁺⁻
Edwin Juvenal Cedeño Herrera⁺⁻
Gloris Cedeño-Batista⁺⁻

DOI https://doi.org/10.48204/j.vian.v7n2.a4566

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DOI: 10.48204/j.vian.v7n2.a4566

Published: 2023-12-29

How to Cite

Batista-Mendoza, G., Cedeño Herrera, E. J., & Cedeño-Batista, G. (2023). Machine learning applied to the analysis of a dataset of environmental parameters in poultry farm. Visión Antataura, 7(2), 121–146. https://doi.org/10.48204/j.vian.v7n2.a4566

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

This article addresses the development of a Machine Learning model applied to a dataset collected on a poultry farm. Its goal is to attain a predictive model based on environmental variables to anticipate forthcoming events. This predictive model aims to optimize decisions linked to the birds' environmental well-being and cut production costs in poultry projects. This investigation obtained information from the "Smart Poultry Farm" system, following the SEMMA methodology and utilizing the Python programming language in the Google Colaboratory IDE environment. The model was built using the binomial logistic regression algorithm in the context of supervised learning. The assessment of the predictive model encompassed the confusion matrix and metrics such as the Overall Quality Index, Accuracy, Sensitivity, Specificity, and F1-Score. Various scenarios were employed to forecast the activation/deactivation of the poultry farm's fans, based on environmental parameters: humidity, temperature, and heat index.

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References

ANAVIP - La avicultura en Panamá. (s. f.). https://www.anavip.org/index.php/la-avicultura-en-panama/
Aviagen. (s. f.). https://aviagen.com/la/tech-center/
Ayoub Shaikh, T., Rasool, T., & Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/ 10.1016/J.COMPAG.2022.107119
Banco Interamericano de Desarrollo. (2022). Clima, capital y un nuevo comienzo para los jóvenes. Boletín mensual. https://www.linkedin.com/pulse/clima-capital-y-un-nuevo-comienzo-para-/
Brownlee, J. (2020). Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python (Machine Learning Mastery, Ed.). https://books.google.com.pa/books?id=uAPuDwAAQBAJ&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false
Cakic, S., Popovic, T., Krco, S., Nedic, D., Babic, D., & Jovovic, I. (2023). Developing Edge AI Computer Vision for Smart Poultry Farms Using Deep Learning and HPC. Sensors 2023, 23(6), 3002. https://doi.org/10.3390/S23063002
Chergui, N., & Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data 9(1), 1-37. https://doi.org/10.1186/S40537-022-00668-2
Cobb. (s. f.). https://www.cobb-vantress.com/resource/featured
Consejo Mexicano de la Carne. (s. f.). https://comecarne.org/
Diez-Olivan, A., Averós, X., Sanz, R., Sierra, B., & Estevez, I. (2019). Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming. Computers and Electronics in Agriculture, 161, 141-150. https://doi.org/ 10.1016/J.COMPAG.2018.03.025
Estrada, M. M. E., Márquez, S., & Restrepo, L. (2007). Efecto de la temperatura y la humedad relativa en los parámetros productivos y la transferencia de calor en pollos de engorde. Revista Colombiana de Ciencias Pecuarias, 20(3), 288-303. https://doi. org/10.17533/UDEA.RCCP
Google Colaboratory. (s. f.). https://colab.research.google.com/?utm_source=scs-index
Hernández-Sampieri, R., y Mendoza Torres, C. P. (2018). Metodología de la Investigación: Las rutas Cuantitativa Cualitativa y Mixta. McGraw-Hil. http://repositorio.uasb. edu.bo:8080/bitstream/54000/1292/1/Hernández- Metodología de la investiga ción.pdf
Larose, D. T., & Larose, C. D. (2014). Discovering Knowledge in Data: An Introduction to Data Mining. 2da Ed. Wiley. https://www.wiley.com/en-us/Discovering+Know ledge+in+%20Data%3A+An+Introduction+to+Data+Mining%2C+2nd+Edition-p-978 0470908747
Ojo, R. O., Ajayi, A. O., Owolabi, H. A., Oyedele, L. O., & Akanbi, L. A. (2022). Internet of Things and Machine Learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, 200, 107266. https://doi.org/10.1016/J.COMPAG.2022.107266
Pajankar, A., & Joshi, A. (2022). Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch. Apress. https://doi.org/10.1007/ 978-1-4842-7921-2
Porcelli, A. M. (2020). Inteligencia Artificial y la Robótica: sus dilemas sociales, éticos y jurídicos. Derecho Global. Estudios sobre Derecho y Justicia, 6(16), 49-105. https://doi.org/10.32870/DGEDJ.V6I16.286
Reier Forradellas, R., Náñez Alonso, S., Jorge-Vazquez, J., & Rodriguez, M. (2020). Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction. Social Sciences, 10(1), 4. https://doi.org/10.3390/SOCSCI10010004
Ribeiro, R., Casanova, D., Teixeira, M., Wirth, A., Gomes, H. M., Borges, A. P., & Enembreck, F. (2019). Generating action plans for poultry management using artificial neural networks. Computers and Electronics in Agriculture, 161, 131-140. https://doi.org/ 10.1016/J.COMPAG.2018.02.017
SAS Help Center: Introduction to SEMMA. (s. f.). https://documentation.sas.com/doc/ en/emref/14.3/n061bzurmej4j3n1jnj8bbjjm1a2.htm
The Caribbean Climate-Smart Accelerator. (2022). Una Hoja De Ruta Climática Para América Latina y El Caribe. Una Hoja De Ruta Climática Para América Latina y El Caribe. https://vimeo.com/695183333
Vuppalapati, C. (2021). Machine Learning and Artificial Intelligence for Agricultural Economics. Springer International Publishing. https://doi.org/10.1007/978-3-030-77485-1
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Wicaksono, D., Perdana, D., & Mayasari, R. (2018). Design and analysis automatic temperature control in the broiler poultry farm based on wireless sensor network. Proceedings - 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017, 2018-Janua, 450-455. https://doi.org/10.1109/ICITISEE.2017.8285549
Zheng, M., Wang, F., Hu, X., Miao, Y., Cao, H., & Tang, M. (2022). A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models. Axioms, 11(11). https://doi.org/10.3390/AXIOMS11110607

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Content Top

[1] ANAVIP - La avicultura en Panamá. (s. f.). https://www.anavip.org/index.php/la-avicultura-en-panama/

[2] Aviagen. (s. f.). https://aviagen.com/la/tech-center/

[3] Ayoub Shaikh, T., Rasool, T., & Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/ 10.1016/J.COMPAG.2022.107119

[4] Banco Interamericano de Desarrollo. (2022). Clima, capital y un nuevo comienzo para los jóvenes. Boletín mensual. https://www.linkedin.com/pulse/clima-capital-y-un-nuevo-comienzo-para-/

[5] Brownlee, J. (2020). Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python (Machine Learning Mastery, Ed.). https://books.google.com.pa/books?id=uAPuDwAAQBAJ&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false

[6] Cakic, S., Popovic, T., Krco, S., Nedic, D., Babic, D., & Jovovic, I. (2023). Developing Edge AI Computer Vision for Smart Poultry Farms Using Deep Learning and HPC. Sensors 2023, 23(6), 3002. https://doi.org/10.3390/S23063002

[7] Chergui, N., & Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data 9(1), 1-37. https://doi.org/10.1186/S40537-022-00668-2

[8] Cobb. (s. f.). https://www.cobb-vantress.com/resource/featured

[9] Consejo Mexicano de la Carne. (s. f.). https://comecarne.org/

[10] Diez-Olivan, A., Averós, X., Sanz, R., Sierra, B., & Estevez, I. (2019). Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming. Computers and Electronics in Agriculture, 161, 141-150. https://doi.org/ 10.1016/J.COMPAG.2018.03.025

[11] Estrada, M. M. E., Márquez, S., & Restrepo, L. (2007). Efecto de la temperatura y la humedad relativa en los parámetros productivos y la transferencia de calor en pollos de engorde. Revista Colombiana de Ciencias Pecuarias, 20(3), 288-303. https://doi. org/10.17533/UDEA.RCCP

[12] Google Colaboratory. (s. f.). https://colab.research.google.com/?utm_source=scs-index

[13] Hernández-Sampieri, R., y Mendoza Torres, C. P. (2018). Metodología de la Investigación: Las rutas Cuantitativa Cualitativa y Mixta. McGraw-Hil. http://repositorio.uasb. edu.bo:8080/bitstream/54000/1292/1/Hernández- Metodología de la investiga ción.pdf

[14] Larose, D. T., & Larose, C. D. (2014). Discovering Knowledge in Data: An Introduction to Data Mining. 2da Ed. Wiley. https://www.wiley.com/en-us/Discovering+Know ledge+in+%20Data%3A+An+Introduction+to+Data+Mining%2C+2nd+Edition-p-978 0470908747

[15] Ojo, R. O., Ajayi, A. O., Owolabi, H. A., Oyedele, L. O., & Akanbi, L. A. (2022). Internet of Things and Machine Learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, 200, 107266. https://doi.org/10.1016/J.COMPAG.2022.107266

[16] Pajankar, A., & Joshi, A. (2022). Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch. Apress. https://doi.org/10.1007/ 978-1-4842-7921-2

[17] Porcelli, A. M. (2020). Inteligencia Artificial y la Robótica: sus dilemas sociales, éticos y jurídicos. Derecho Global. Estudios sobre Derecho y Justicia, 6(16), 49-105. https://doi.org/10.32870/DGEDJ.V6I16.286

[18] Reier Forradellas, R., Náñez Alonso, S., Jorge-Vazquez, J., & Rodriguez, M. (2020). Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction. Social Sciences, 10(1), 4. https://doi.org/10.3390/SOCSCI10010004

[19] Ribeiro, R., Casanova, D., Teixeira, M., Wirth, A., Gomes, H. M., Borges, A. P., & Enembreck, F. (2019). Generating action plans for poultry management using artificial neural networks. Computers and Electronics in Agriculture, 161, 131-140. https://doi.org/ 10.1016/J.COMPAG.2018.02.017

[20] SAS Help Center: Introduction to SEMMA. (s. f.). https://documentation.sas.com/doc/ en/emref/14.3/n061bzurmej4j3n1jnj8bbjjm1a2.htm

[21] The Caribbean Climate-Smart Accelerator. (2022). Una Hoja De Ruta Climática Para América Latina y El Caribe. Una Hoja De Ruta Climática Para América Latina y El Caribe. https://vimeo.com/695183333

[22] Vuppalapati, C. (2021). Machine Learning and Artificial Intelligence for Agricultural Economics. Springer International Publishing. https://doi.org/10.1007/978-3-030-77485-1

[23] ?

[24] Wicaksono, D., Perdana, D., & Mayasari, R. (2018). Design and analysis automatic temperature control in the broiler poultry farm based on wireless sensor network. Proceedings - 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017, 2018-Janua, 450-455. https://doi.org/10.1109/ICITISEE.2017.8285549

[25] Zheng, M., Wang, F., Hu, X., Miao, Y., Cao, H., & Tang, M. (2022). A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models. Axioms, 11(11). https://doi.org/10.3390/AXIOMS11110607

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