Visión por Computadora en el ámbito de control productivo y sanidad en Animales de Granja

Autores/as

  • Jeidy Katherine Reyes Zambrano Universidad Técnica de Manabí, Facultad de Ciencias Informáticas, Departamento de Sistemas Computacionales, Portoviejo, Manabi, Ecuador. https://orcid.org/0009-0000-5203-9212
  • María Genoveva Moreira Santos Universidad Técnica de Manabí, Facultad de Ciencias Informáticas, Departamento de Sistemas Computacionales, Portoviejo, Manabi, Ecuador. https://orcid.org/0000-0003-3216-9831

DOI:

https://doi.org/10.33936/isrtic.v9i1.7156

Palabras clave:

Visión por computadora, granja, animales, bienestar, salud

Resumen

En los últimos años, las tecnologías de visión por computadora han ganado popularidad en el ámbito agrícola; sin embargo, el enfoque en animales está en desarrollo debido a los desafíos que implica su integración. Esta investigación se realiza con el objetivo de estudiar el uso de tecnologías de visión por computadora para el manejo productivo y sanitario de animales de granja, para ello se utilizó la metodología “Systematic Literature Review” propuesta por Kitchenham & Charters, la cual consiste en realizar una búsqueda exhaustiva a través de bases de datos científicas, para este estudio se utilizó Scopus, IEEE Xplore, ScienceDirect y Google Scholar siguiendo criterios específicos de inclusión y exclusión. El proceso incluye la formulación de preguntas de investigación, la identificación de palabras claves relevantes y la evaluación de la calidad de los estudios seleccionados. A través de este proceso, se garantiza un análisis exhaustivo y riguroso de la literatura existente, logrando obtener resultados precisos y relevantes con la finalidad de estudiar diversos problemas con animales de granja tales como pollos, cerdos y ganado, que producen materia prima para el sustento y negocios de familias en sectores rurales. Tareas como la identificación, y rastreo de animales es posible gracias a herramientas como YOLOv, CNN, técnicas de segmentación, colorimetría, imágenes 2D, 3D, y cámaras que sirven como entrada de datos que se convierten en información procesada para el análisis y toma de decisiones inteligentes. Estas herramientas son útiles en prácticas con distintos propósitos que engloban el bienestar, la sanidad y la productividad de los animales. Además, esta investigación recomienda herramientas que podrían competir o superar las soluciones actuales, aportando a la ganadería de precisión en la mejora continua del uso tecnológico y su relación con el agricultor.

Descargas

La descarga de datos todavía no está disponible.

Citas

Abd Aziz, N. S. N., Mohd Daud, S., Dziyauddin, R. A., Adam, M. Z., & Azizan, A. (2021). A Review on Computer Vision Technology for Monitoring Poultry Farm - Application, Hardware, and Software. In IEEE Access (Vol. 9, pp. 12431–12445). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2020.304781826

Allueva Molina, Q., Ko, H. L., Gómez, Y., Manteca, X., & Llonch, P. (2023b). Comparative study between scan sampling behavioral observations and an automatic monitoring image system on a commercial fattening pig farm. Frontiers in Animal Science, 4, 1248972. https://doi.org/10.3389/fanim.2023.1248972

Allueva Molina, Q., Ko, H. L., Gómez, Y., Manteca, X., & Llonch, P. (2023a). Comparative study between scan sampling behavioral observations and an automatic monitoring image system on a commercial fattening pig farm. Frontiers in Animal Science, 4, 1248972. https://doi.org/10.3389/FANIM.2023.1248972/BIBTEX

Almadani, I., Ramos, B., Abuhussein, M., & Robinson, A. L. (2024). Advanced Swine Management: Infrared Imaging for Precise Localization of Reproductive Organs in Livestock Monitoring. Digital, 4(2), 446–460. https://doi.org/10.3390/digital4020022

Alsahaf, A., Azzopardi, G., Ducro, B., Hanenberg, E., Veerkamp, R. F., & Petkov, N. (2019). Estimation of Muscle Scores of Live Pigs Using a Kinect Camera. IEEE Access, 7, 52238–52245. https://doi.org/10.1109/ACCESS.2019.2910986

Atkinson, G. A., Smith, L. N., Smith, M. L., Reynolds, C. K., Humphries, D. J., Moorby, J. M., Leemans, D. K., & Kingston-Smith, A. H. (2020). A computer vision approach to improving cattle digestive health by the monitoring of faecal samples (google translator, Trans.). Scientific Reports, 10(1), 1–12. https://doi.org/10.1038/s41598-020-74511-0

Bergman, N., Yitzhaky, Y., & Halachmi, I. (2024). Biometric identification of dairy cows via real-time facial recognition. Animal, 18(3), 101079. https://doi.org/10.1016/j.animal.2024.101079

Bery, S., Brown-Brandl, T. M., Jones, B. T., Rohrer, G. A., & Sharma, S. R. (2023). Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision. Animals, 14(1), 131. https://doi.org/10.3390/ANI14010131

Bhuiyan, M. R., & Wree, P. (2023). Animal Behavior for Chicken Identification and Monitoring the Health Condition Using Computer Vision: A Systematic Review (google translator, Trans.). IEEE Access, 11, 126601–126610. https://doi.org/10.1109/ACCESS.2023.3331092

Braithwaite, I., Blanke, M., Zhang, G. Q., & Carstensen, J. M. (2005). Design of a vision-based sensor for autonomous pig house cleaning (google translate, Trans.). Eurasip Journal on Applied Signal Processing, 2005(13), 2005–2017. https://doi.org/10.1155/ASP.2005.2005/METRICS

Caffarini, J. G., Bresolin, T., & Dorea, J. R. R. (2022). Predicting ribeye area and circularity in live calves through 3D image analyses of body surface. Journal of Animal Science, 100(9), skac242. https://doi.org/10.1093/jas/skac242

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, 23(6), 3002. https://doi.org/10.3390/S23063002

Chae, J. W., Sim, H. S., Lee, C. W., Choi, C. S., & Cho, H. C. (2024). Video-Based Analysis of Cattle Behaviors: Improved Classification Using FlowEQ Transform (google translator, Trans.). IEEE Access, 12, 42860–42867. https://doi.org/10.1109/ACCESS.2024.3379277

Chakraborty, S., Karthik, K., & Banik, S. (2021). Graph Synthesis for Pig Breed Classification from Muzzle Images. IEEE Access, 9, 127240–127258. https://doi.org/10.1109/ACCESS.2021.3111957

Chen, C. P. J., Morota, G., Lee, K., Zhang, Z., & Cheng, H. (2022a). VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera (google translator, Trans.). Journal of Animal Science, 100(6), 1–10. https://doi.org/10.1093/jas/skac147

Chen, C. P. J., Morota, G., Lee, K., Zhang, Z., & Cheng, H. (2022b). VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera. Journal of Animal Science, 100(6), 1–10. https://doi.org/10.1093/jas/skac147

Dos Santos, C. A., Landim, N. M. D., de Araújo, H. X., & Paim, T. D. P. (2022). Automated Systems for Estrous and Calving Detection in Dairy Cattle. AgriEngineering, 4(2), 475–482. https://doi.org/10.3390/AGRIENGINEERING4020031

Fang, C., Zheng, H., Yang, J., Deng, H., & Zhang, T. (2022). Study on Poultry Pose Estimation Based on Multi-Parts Detection. Animals, 12(10). https://doi.org/10.3390/ani12101322

Feng, W., Wang, K., & Zhou, S. (2023). An Efficient Neural Network for Pig Counting and Localization by Density Map Estimation. IEEE Access, 11, 81079–81091. https://doi.org/10.1109/ACCESS.2023.3297141

Fernandes, A. F. A., Dórea, J. R. R., & Rosa, G. J. de M. (2020). Image Analysis and Computer Vision Applications in Animal Sciences: An Overview. Frontiers in Veterinary Science, 7, 551269. https://doi.org/10.3389/FVETS.2020.551269/BIBTEX

Ferri, F., Yepez, J., Ahadi, M., Wang, Y., Ko, R., Seddon, Y. M., & Ko, S. B. (2024). Enhancing welfare assessment: Automated detection and imaging of dorsal and lateral views of swine carcasses for identification of welfare indicators. Computers and Electronics in Agriculture, 222, 109058. https://doi.org/10.1016/j.compag.2024.109058

Fuentes, S., Viejo, C. G., Chauhan, S. S., Joy, A., Tongson, E., & Dunshea, F. R. (2020). Non-invasive sheep biometrics obtained by computer vision algorithms and machine learning modeling using integrated visible/infrared thermal cameras. Sensors (Switzerland), 20(21), 1–18. https://doi.org/10.3390/s20216334

Guo, Y., Aggrey, S. E., Wang, P., Oladeinde, A., & Chai, L. (2022). Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning. Animals, 12(23), 3390. https://doi.org/10.3390/ani12233390

Hansen, M. F., Baxter, E. M., Rutherford, K. M. D., Futro, A., Smith, M. L., & Smith, L. N. (2021). Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs. Agriculture, 11(9), 847. https://doi.org/10.3390/AGRICULTURE11090847

Hayden, M. A., Barim, M. S., Weaver, D. L., Elliott, K. C., Flynn, M. A., & Lincoln, J. M. (2022). Occupational Safety and Health with Technological Developments in Livestock Farms: A Literature Review. International Journal of Environmental Research and Public Health, 19(24), 16440. https://doi.org/10.3390/IJERPH192416440

Hervy Paternina Pedroza, José Linares Morales, & Katherine Hernández Ayala. (2019). Transferencia de tecnología y conocimientos en el sector explotador de bovinos. Google Scholar, 4(1), 11. https://doi.org/10.25214/27114406.936

Jorquera-Chavez, M., Fuentes, S., Dunshea, F. R., Warner, R. D., Poblete, T., & Jongman, E. C. (2019). Modelling and validation of computer vision techniques to assess heart rate, eye temperature, ear-base temperature and respiration rate in cattle (google translator, Trans.). Animals, 9(12), 18. https://doi.org/10.3390/ani9121089

Juárez, R. J. (2023). Ganadería de precisión, una revisión a los avances dentro de la avicultura enfocados a la crianza de pollos de engorde. Prisma Tecnológico, 14(1), 38–48. https://doi.org/10.33412/pri.v14.1.3652

Kim, J., Suh, Y., Lee, J., Chae, H., Ahn, H., Chung, Y., & Park, D. (2022). EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board (google translators, Trans.). Sensors, 22(7), 1–17. https://doi.org/10.3390/s22072689

Lee, S., Ahn, H., Seo, J., Chung, Y., Park, D., & Pan, S. (2019). Practical Monitoring of Undergrown Pigs for IoT-Based Large-Scale Smart Farm (google translator, Trans.). IEEE Access, 7, 173796–173810. https://doi.org/10.1109/ACCESS.2019.2955761

Lei, K., Zong, C., Du, X., Teng, G., & Feng, F. (2021). Oestrus Analysis of Sows Based on Bionic Boars and Machine Vision Technology. Animals, 11(6), 1485. https://doi.org/10.3390/ANI11061485

Li, K., Teng, G., Wang, J., Zhang, Y., Gao, L., & Feng, H. (2024). Body Condition Scoring of Dairy Cows Based on Feature Point Location. IEEE Access, 12, 5270–5283. https://doi.org/10.1109/ACCESS.2023.3349320

Li, G., Huang, Y., Chen, Z., Chesser, G. D., Purswell, J. L., Linhoss, J., & Zhao, Y. (2021). Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review. Sensors, 21(4), 1–42. https://doi.org/10.3390/s21041492

Marcos Darío Aranda. (2022, October 3). Vista de Aprendizaje Automático aplicado a la calidad del desarrollo en la ganadería de precisión. AKAJea. https://doi.org/10.33414/ajea.1040.2022

Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Big data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96(4), 1540–1550. https://doi.org/10.1093/jas/sky014

Na, M. H., Cho, W. H., Kim, S. K., & Na, I. S. (2022). Automatic Weight Prediction System for Korean Cattle Using Bayesian Ridge Algorithm on RGB-D Image. Electronics, 11(10), 1–22. https://doi.org/10.3390/electronics11101663

Nakrosis, A., Paulauskaite-Taraseviciene, A., Raudonis, V., Narusis, I., Gruzauskas, V., Gruzauskas, R., & Lagzdinyte-Budnike, I. (2023). Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification. Animals, 13(19), 1. https://doi.org/10.3390/ani13193041

Odo, A., Muns, R., Boyle, L., & Kyriazakis, I. (2023). Video Analysis Using Deep Learning for Automated Quantification of Ear Biting in Pigs (google translator, Trans.). IEEE Access, 11, 59744–59757. https://doi.org/10.1109/ACCESS.2023.3285144

Paudel, S., de Sousa, R. V., Sharma, S. R., & Brown-Brandl, T. (2023). Deep Learning Models to Predict Finishing Pig Weight Using Point Clouds. Animals, 14(1), 31. https://doi.org/10.3390/ANI14010031

Pretto, A., Savio, G., Gottardo, F., Uccheddu, F., & Concheri, G. (2024). A novel low-cost visual ear tag based identification system for precision beef cattle livestock farming (google translator, Trans.). Information Processing in Agriculture, 11(1), 117–126. https://doi.org/10.1016/j.inpa.2022.10.003

Pu, J., Yu, C., Chen, X., Zhang, Y., Yang, X., & Li, J. (2022). Research on Chengdu Ma Goat Recognition Based on Computer Vison (google translation, Trans.). Animals, 12(14), 1746. https://doi.org/10.3390/ANI12141746

Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine Learning Applications for Precision Agriculture: A Comprehensive Review (google translator, Trans.). IEEE Access, 9, 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415

Unal, Z. (2020). Smart Farming Becomes even Smarter with Deep Learning - A Bibliographical Analysis (Google Translator, Trans.). IEEE Access, 8, 105587–105609. https://doi.org/10.1109/ACCESS.2020.3000175

Urquilla Castaneda, A., Matías Delgado, J., Salvador, E., & Gavidia, F. (2023). ¿Será la Agricultura 4.0 la solución al hambre global? Will Agriculture 4.0 be the solution to global hunger? CENtroAmériCA. rEviStA SEmEStrAl ENEro-JuNio, 23. https://doi.org/10.5377/ryr.v1i57.16696

Wurtz, K., Camerlink, I., D’Eath, R. B., Fernández, A. P., Norton, T., Steibel, J., & Siegford, J. (2019). Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review (español, Trans.). PLoS ONE, 14(12), 1–35. https://doi.org/10.1371/journal.pone.0226669

Yao, Y., Yu, H., Mu, J., Li, J., & Pu, H. (2020). Estimation of the Gender Ratio of Chickens Based on Computer Vision: Dataset and Exploration. Entropy, 22(7), 719. https://doi.org/10.3390/E22070719

You, M., Liu, J., Zhang, J., Xv, M., & He, D. (2020). A novel chicken meat quality evaluation method based on color card localization and color correction (google translator, Trans.). IEEE Access, 8, 170093–170100. https://doi.org/10.1109/ACCESS.2020.2989439

Zhang, W., Wang, Y., Guo, L., Falzon, G., Kwan, P., Jin, Z., Li, Y., & Wang, W. (2024). Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning (google translator, Trans.). Animals, 14(9), 1–15. https://doi.org/10.3390/ani14091324

Zu, L., Chu, X., Wang, Q., Ju, Y., & Zhang, M. (2023). Joint Feature Target Detection Algorithm of Beak State Based on YOLOv5 (google translator, Trans.). IEEE Access, 11, 64458–64467. https://doi.org/10.1109/ACCESS.2023.327543229

Publicado

2025-01-02

Cómo citar

[1]
Reyes Zambrano, J.K. y Moreira Santos, M.G. 2025. Visión por Computadora en el ámbito de control productivo y sanidad en Animales de Granja. Informática y Sistemas. 9, 1 (ene. 2025), 16–29. DOI:https://doi.org/10.33936/isrtic.v9i1.7156.

Número

Sección

Artículos regulares