Non-invasive Monitoring in Guinea Pigs Production to Assist in Establishing Their Health Status: A Literature Review

John Barco; William Arévalo; Héctor Mora-Paz; Miller Ruales; José Camilo Eraso Guerrero

doi:10.53588/alpa.330402

John Barco john.barco@unad.edu.co https://orcid.org/0000-0002-9152-2132
William Arévalo Universidad CESMAG, Facultad de Ingeniería https://orcid.org/0000-0003-4347-1948
Héctor Mora-Paz Universidad de Nariño
Miller Ruales Universidad CESMAG, Facultad de Ingeniería https://orcid.org/0009-0001-2496-7959
José Camilo Eraso Guerrero Universidad CESMAG https://orcid.org/0000-0002-2264-6385

DOI: https://doi.org/10.53588/alpa.330402

Keywords: cavia porcellus, guinea pig, health status, animal monitoring

Abstract

Guinea pigs have significant economic and cultural importance in Latin America. However, their breeding is affected by various diseases that reduce productivity and animal welfare. Early detection of these diseases is crucial, but traditional methods such as palpation and visual observation can be imprecise and subjective. Additionally, guinea pigs are very sensitive to human contact, and improper handling can negatively affect their health. In this context, the question arises: Is it possible to develop a non-invasive monitoring system that combines temperature and weight measurements in guinea pigs to determine their health status more accurately than traditional methods? To answer this question, a comprehensive review of the existing literature is proposed. This review aims to identify and analyze the most relevant non-invasive technologies for measuring the temperature and weight of these animals. Furthermore, methodologies for combining these measurements to assess the health status of guinea pigs will be explored. The review will also consider current trends in artificial intelligence, specifically in machine learning, to analyze the data collected by sensors. The review methodology follows a systematic approach, establishing inclusion and exclusion criteria to ensure the quality and relevance of the information collected. Keywords are defined, and relevant research databases are selected. The article selection process is carried out in three stages: elimination of duplicates, elimination of articles with access restrictions, and filtering according to the research topic. Finally, the quality of the selected articles is evaluated to deepen the proposed discussion and draw conclusions on the feasibility and potential benefits of implementing a non-invasive monitoring system to improve guinea pig health and welfare in production settings.

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References

Achour, Brahim, Malika Belkadi, Rachida Aoudjit, Mourad Laghrouche, Mustapha Lalam, y Mehammed Daoui. 2022. «Classification of dairy cows’ behavior by energy-efficient sensor». Journal of Reliable Intelligent Environments 8(2):165-82. doi:10.1007/s40860-021-00144-3.

Alonso, Ricardo S., Inés Sittón-Candanedo, Óscar García, Javier Prieto, y Sara Rodríguez-González. 2020. «An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario». Ad Hoc Networks 98:102047. doi:10.1016/j.adhoc.2019.102047.

Barco-Jiménez, J., M. Martínez, y A. L. Solarte. 2021. «Sistema de pesaje automatizado que facilita el manejo de cuyes (Cavia porcellus)». Archivos de Zootecnia 70(269):112-16. doi:10.21071/az.v70i269.5425.

Cesar Guerra León. 2009. Manual técnico de crianza de cuyes. Cajamarca: CEDEPAS Norte.

Chung, Hanwook, Jingjie Li, Younghyun Kim, Jennifer M. C. Van Os, Sabrina H. Brounts, y Christopher Y. Choi. 2020. «Using implantable biosensors and wearable scanners to monitor dairy cattle’s core body temperature in real-time». Computers and Electronics in Agriculture 174(April):105453. doi:10.1016/j.compag.2020.105453.

Cotticelli, Alessio, Maria Teresa Verde, Annalisa Liccardo, Giorgio De Alteriis, Francesco Lamonaca, Roberta Matera, Gianluca Neglia, Tanja Peric, Alberto Prandi, y Francesco Bonavolontà. 2023. «On the use of 3D camera to accurately measure volume and weight of dairy cow feed». Acta IMEKO 12(4):1-6. doi:10.21014/actaimeko.v12i4.1633.

Dineva, Kristina, y Tatiana Atanasova. 2023. «and Data Management on AWS Cloud».

Dos Reis, B. R., Z. Easton, R. R. White, y D. Fuka. 2021. «A LoRa sensor network for monitoring pastured livestock location and activity». Translational Animal Science 5(2):1-9. doi:10.1093/tas/txab010.

Dutta, Debeshi, Dwipjyoti Natta, Soumen Mandal, y Nilotpal Ghosh. 2022. «MOOnitor: An IoT based multi-sensory intelligent device for cattle activity monitoring». Sensors and Actuators A: Physical 333:113271. doi:10.1016/j.sna.2021.113271.

Echegaray, Noem, Abdo Hassoun, Sandeep Jagtap, Michelle Tetteh-caesar, Manoj Kumar, Igor Tomasevic, Gulden Goksen, y Jose Manuel Lorenzo. 2022. «applied sciences Technologies in the Meat Industry». 1-19.

Gao, Ronghua, Qihang Liu, Qifeng Li, Jiangtao Ji, Qiang Bai, Kaixuan Zhao, y Liuyiyi Yang. 2023. «Multi-Target Rumination Behavior Analysis Method of Cows Based on Target Detection and Optical Flow Algorithm». Sustainability (Switzerland) 15(18). doi:10.3390/su151814015.

Gong, Bo, Kanyuan Dai, Ji Shao, Ling Jing, y Yingyi Chen. 2023. «Fish-TViT: A novel fish species classification method in multi water areas based on transfer learning and vision transformer». Heliyon 9(6):e16761. doi:10.1016/j.heliyon.2023.e16761.

Hansen, Mark F., Alphonsus Oparaeke, Ryan Gallagher, Amir Karimi, Fahim Tariq, y Melvyn L. Smith. 2022. «Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights». Frontiers in Veterinary Science 9(February):1-7. doi:10.3389/fvets.2022.835529.

Hever Castro. 2002. Sistemas de crianza de cuyes a nivel familiar-comercial en el sector rural. Provo, Utah, USA: Benson Agriculture and Food Institute.

Hua Luo Shuting Cheng, Zhou Jiang Wang Hou, y Zhengrong Wang. 2021. «Economical bluetooth low energy-based telemetry system with combined data processing method for long-term laboratory animal monitoring for biological rhythm research». Chronobiology International 38(3):451-65. doi:10.1080/07420528.2020.1868489.

Isabel Ramos. 2014. Crianza, producción y comercialización de cuyes. 1ra ed. Lima, Perú: MACRO.

Jebari, Hakim, Meriem Hayani Mechkouri, Siham Rekiek, y Kamal Reklaoui. 2023. «Poultry-Edge-AI-IoT System for Real-Time Monitoring and Predicting by Using Artificial Intelligence». International Journal of Interactive Mobile Technologies 17(12):149-70. doi:10.3991/ijim.v17i12.38095.

Kitchenham, Barbara, O. Pearl Brereton, David Budgen, Mark Turner, John Bailey, y Stephen Linkman. 2009. «Systematic Literature Reviews in Software Engineering – A Systematic Literature Review». Information and Software Technology 51(1):7-15. doi:10.1016/j.infsof.2008.09.009.

Li, Shijun, Lili Fu, Yu Sun, Ye Mu, Lin Chen, Ji Li, y He Gong. 2021. «Individual dairy cow identification based on lightweight convolutional neural network». PLoS ONE 16(11 November):1-14. doi:10.1371/journal.pone.0260510.

Liu, Xiaofeng, Ziyang Wang, Jie Li, Angelo Cangelosi, y Chenguang Yang. 2023. «Demonstration Learning and Generalization of Robotic Motor Skills Based on Wearable Motion Tracking Sensors». IEEE Transactions on Instrumentation and Measurement 72:1-15. doi:10.1109/TIM.2023.3288240.

Mateo-Fornés, Jordi, Adela Pagès-Bernaus, Lluís Miquel Plà-Aragonés, Joan Pau Castells-Gasia, y Daniel Babot-Gaspa. 2021. «An internet of things platform based on microservices and cloud paradigms for livestock». Sensors 21(17):1-22. doi:10.3390/s21175949.

Morchid, Abdennabi, Rachid El Alami, Aeshah A. Raezah, y Yassine Sabbar. 2024. «Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges». Ain Shams Engineering Journal 15(3):102509. doi:10.1016/j.asej.2023.102509.

Neethirajan, Suresh. 2023. «Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation». Sensors 23(16). doi:10.3390/s23167045.

Okokpujie, Kennedy, Imhade P. Okokpujie, Adebayo Tunbosun Ogundipe, Chukwuka Daniel Anike, Obedafe Blessed Asaboro, y Akingunsoye Adenugba Vincent. 2023. «Development of a Sustainable Internet of Things-Based System for Monitoring Cattle Health and Location with Web and Mobile Application Feedback». Mathematical Modelling of Engineering Problems 10(3):740-48. doi:10.18280/mmep.100302.

Pagano, Antonino, Daniele Croce, Ilenia Tinnirello, y Gianpaolo Vitale. 2023. «A Survey on LoRa for Smart Agriculture: Current Trends and Future Perspectives». IEEE Internet of Things Journal 10(4):3664-79. doi:10.1109/JIOT.2022.3230505.

Peng, Yuping, Zhixiong Zeng, Enli Lv, Xinyuan He, Boyang Zeng, Fan Wu, Jiaming Guo, y Ziwei Li. 2022. «A Real-Time Automated System for Monitoring Individual Feed Intake and Body Weight of Group-Housed Young Chickens». Applied Sciences (Switzerland) 12(23). doi:10.3390/app122312339.

Ren, Yue, Douglas Duhatschek, Claudio C. Bartolomeu, David Erickson, y Julio O. Giordano. 2023. «An automated system for cattle reproductive management under the IoT framework. Part I: the e-Synch system and cow responses». Frontiers in Animal Science 4(March):1-11. doi:10.3389/fanim.2023.1093851.

Revelo-Sánchez, Oscar, César A. Collazos-Ordóñez, y Javier A. Jiménez-Toledo. 2018. «El trabajo colaborativo como estrategia didáctica para la enseñanza/aprendizaje de la programación: una revisión sistemática de literatura». TecnoLógicas 21(41):115-34. doi:10.22430/22565337.731.

RSPCA. 2015. Care for your guinea pigs. London, England: Harper Collins.

Saravanan, K., y S. Saraniya. 2018. «Cloud IOT based novel livestock monitoring and identification system using UID». Sensor Review 38(1):21-33. doi:10.1108/SR-08-2017-0152.

Tekın, Koray, Begüm Yurdakök-Dıkmen, Halit Kanca, y Raphaël Guatteo. 2021. «Precision livestock farming technologies: Novel direction of information flow». Ankara Universitesi Veteriner Fakultesi Dergisi 68(2):193-212. doi:10.33988/auvfd.837485.

Xiong, Xingguo, Mingzhou Lu, Weizhong Yang, Guanghui Duan, Qingyan Yuan, Mingxia Shen, Tomas Norton, y Daniel Berckmans. 2019. «An Automatic Head Surface Temperature Extraction Individual Broiler». Sensors 19(5286).

Yang, Hongbo, y Shi Qiu. 2024. «A Novel Dynamic Contextual Feature Fusion Model for Small Object Detection in Satellite Remote-Sensing Images». Information 15(4):230. doi:10.3390/info15040230.

Yin, Xuqiang, Dihua Wu, Yuying Shang, Bo Jiang, y Huaibo Song. 2020. «Using an EfficientNet-LSTM for the recognition of single Cow’s motion behaviours in a complicated environment». Computers and Electronics in Agriculture 177(June):105707. doi:10.1016/j.compag.2020.105707.

Zhang, Mengjie, Huanhuan Feng, Hailing Luo, Zhigang Li, y Xiaoshuan Zhang. 2020. «Comfort and health evaluation of live mutton sheep during the transportation based on wearable multi-sensor system». Computers and Electronics in Agriculture 176(July):105632. doi:10.1016/j.compag.2020.105632.

Zhao, Hongke, Rui Mao, Mei Li, Bin Li, y Meili Wang. 2023. «SheepInst: A High-Performance Instance Segmentation of Sheep Images Based on Deep Learning». Animals 13(8):1-19. doi:10.3390/ani13081338.

Zhao, Xinyue, Ryou Tanaka, Ahmed S. Mandour, Kazumi Shimada, y Lina Hamabe. 2024. «Remote Vital Sensing in Clinical Veterinary Medicine: A Comprehensive Review of Recent Advances, Accomplishments, Challenges and Future Perspectives».

Zheng, Haikun, Tiemin Zhang, Cheng Fang, Jiayuan Zeng, y Xiuli Yang. 2021. «Design and implementation of poultry farming information management system based on cloud database». Animals 11(3):1-15. doi:10.3390/ani11030900.