Automated monitoring system for estrus signs in cattle using precision livestock farming with IoT technology in the Peruvian Amazon
DOI:
https://doi.org/10.51252/rcsi.v5i1.837Keywords:
Estrus detection, precision livestock farming, Internet of Things, LoRa, LoRaWANAbstract
Estrus detection is key to optimizing conception in cows and improving livestock reproductive efficiency. The conventional method requires continuous observation, demanding labor and time. We developed an IoT-based system that automates estrus monitoring using a multisensor device mounted on the cow’s neck. It collects data and transmits it via LoRaWAN to a Gateway, which forwards it to The Things Stack and then to TagoIO for visualization and storage. In field tests, after synchronizing estrus in a cow in the Peruvian Amazon, data was collected and analyzed. The system recorded physiological and behavioral information, showing that within 72 hours, movement and body temperature increased, indicating estrus.
References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys and Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095 DOI: https://doi.org/10.1109/COMST.2015.2444095
Al-Gumaei, K., Schuba, K., Friesen, A., Heymann, S., Pieper, C., Pethig, F., & Schriegel, S. (2018). A Survey of Internet of Things and Big Data integrated Solutions for Industrie 4.0. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2018-September, 1417–1424. https://doi.org/10.1109/ETFA.2018.8502484 DOI: https://doi.org/10.1109/ETFA.2018.8502484
Al-Samman, A. M., Al-Hadhrami, T., Al Shami, A., Alnajjar, F., M Almuhaya, M. A., Jabbar, W. A., Sulaiman, N., & Abdulmalek, S. (2022). A Survey on LoRaWAN Technology: Recent Trends, Opportunities, Simulation Tools and Future Directions. Electronics, 11(1), 164. https://doi.org/10.3390/ELECTRONICS11010164 DOI: https://doi.org/10.3390/electronics11010164
Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., & Pugliese, C. (2022). Review: Precision Livestock Farming technologies in pasture-based livestock systems. Animal, 16(1), 100429. https://doi.org/10.1016/J.ANIMAL.2021.100429 DOI: https://doi.org/10.1016/j.animal.2021.100429
Arablouei, R., Wang, Z., Bishop-Hurley, G. J., & Liu, J. (2023). Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data. Smart Agricultural Technology, 4, 100163. https://doi.org/10.1016/J.ATECH.2022.100163 DOI: https://doi.org/10.1016/j.atech.2022.100163
Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the Agriculture 4.0 Landscape. Emerging Trends, Challenges and Opportunities. Agronomy, 11(4), 667. https://doi.org/10.3390/AGRONOMY11040667 DOI: https://doi.org/10.3390/agronomy11040667
Arya, D., Goswami, R., & Sharma, M. (2023). Estrous synchronization in cattle, sheep and goat. Multidisciplinary Reviews, 6(1), 2023001–2023001. https://doi.org/10.31893/MULTIREV.2023001 DOI: https://doi.org/10.31893/multirev.2023001
Astill, J., Dara, R. A., Fraser, E. D. G., Roberts, B., & Sharif, S. (2020). Smart poultry management: Smart sensors, big data, and the internet of things. Computers and Electronics in Agriculture, 170. https://doi.org/https://doi.org/10.1016/j.compag.2020.105291 DOI: https://doi.org/10.1016/j.compag.2020.105291
Benjamin, M., & Yik, S. (2019). Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners. Animals, 9(4), 133. https://doi.org/10.3390/ANI9040133 DOI: https://doi.org/10.3390/ani9040133
Dineva, K., & Atanasova, T. (2021). Design of Scalable IoT Architecture Based on AWS for Smart Livestock. Animals, 11(9), 2697. https://doi.org/10.3390/ANI11092697 DOI: https://doi.org/10.3390/ani11092697
Domínguez-Bolaño, T., Campos, O., Barral, V., Escudero, C. J., & García-Naya, J. A. (2022). An overview of IoT architectures, technologies, and existing open-source projects. Internet of Things, 20, 100626. https://doi.org/10.1016/J.IOT.2022.100626 DOI: https://doi.org/10.1016/j.iot.2022.100626
Eckelkamp, E. A. (2019). Invited Review: Current state of wearable precision dairy technologies in disease detection. Applied Animal Science, 35(2), 209–220. https://doi.org/10.15232/AAS.2018-01801 DOI: https://doi.org/10.15232/aas.2018-01801
Firk, R., Stamer, E., Junge, W., & Krieter, J. (2002). Automation of oestrus detection in dairy cows: a review. Livestock Production Science, 75(3), 219–232. https://doi.org/10.1016/S0301-6226(01)00323-2 DOI: https://doi.org/10.1016/S0301-6226(01)00323-2
Food and Agriculture Organization. (2016). The State of Food and Agriculture. Climate change, agriculture and food security. In Food and Agriculture Organization of the united Nations. Food and Agriculture Organization of the United Nations. https://www.fao.org/agrifood-economics/publications/detail/en/c/447313/
Gargiulo, J. I., Eastwood, C. R., Garcia, S. C., & Lyons, N. A. (2018). Dairy farmers with larger herd sizes adopt more precision dairy technologies. Journal of Dairy Science, 101(6), 5466–5473. https://doi.org/10.3168/JDS.2017-13324 DOI: https://doi.org/10.3168/jds.2017-13324
Gkotsiopoulos, P., Zorbas, D., & Douligeris, C. (2021). Performance determinants in LoRa networks: A literature review. IEEE Communications Surveys and Tutorials, 23(3), 1721–1758. https://doi.org/10.1109/COMST.2021.3090409 DOI: https://doi.org/10.1109/COMST.2021.3090409
Gündüz, K. A., & Başçiftçi, F. (2023). Collecting information on estrus in cattle using the internet of things. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 75(4), 599–599. https://doi.org/10.1590/1678-4162-12940 DOI: https://doi.org/10.1590/1678-4162-12940
Hassan-Vásquez, J. A., Maroto-Molina, F., & Guerrero-Ginel, J. E. (2022). GPS Tracking to Monitor the Spatiotemporal Dynamics of Cattle Behavior and Their Relationship with Feces Distribution. Animals, 12(18), 2383. https://doi.org/10.3390/ANI12182383/S1 DOI: https://doi.org/10.3390/ani12182383
Heo, E., Ahn, S.-J., & CHOI, K.-S. (2019). Real-Time Cattle Action Recognition for Estrus Detection. KSII Transactions on Internet and Information Systems, 13(4), 2148–2161. DOI: https://doi.org/10.3837/tiis.2019.04.023
Chaudhry, A. A., Mumtaz, R., Hassan Zaidi, S. M., Tahir, M. A., & Muzammil School, S. H. (2020). Internet of Things (IoT) and Machine Learning (ML) enabled Livestock Monitoring. HONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI, 151–155. https://doi.org/10.1109/HONET50430.2020.9322666
Jónsson, R., Blanke, M., Poulsen, N. K., Caponetti, F., & Højsgaard, S. (2011). Oestrus detection in dairy cows from activity and lying data using on-line individual models. Computers and Electronics in Agriculture, 76(1), 6–15. https://doi.org/10.1016/J.COMPAG.2010.12.014 DOI: https://doi.org/10.1016/j.compag.2010.12.014
Jouhari, M., Saeed, N., Alouini, M. S., & Amhoud, E. M. (2023). A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges. IEEE Communications Surveys and Tutorials, 25(3), 1841–1876. https://doi.org/10.1109/COMST.2023.3274934 DOI: https://doi.org/10.1109/COMST.2023.3274934
Kanagachidambaresan, G. R. (2021). Introduction to KiCad Design for Breakout and Circuit Designs. Internet of Things, 165–175. https://doi.org/10.1007/978-3-030-72957-8_8 DOI: https://doi.org/10.1007/978-3-030-72957-8_8
Kaya, A., Güneş, E., & Memili, E. (2018). Application of reproductive biotechnologies for sustainableproduction of livestock in Turkey. Turkish Journal of Veterinary & Animal Sciences, 42(3), 143–151. https://doi.org/10.3906/vet-1706-66 DOI: https://doi.org/10.3906/vet-1706-66
Koçyiğit, R., Yanar, M., Diler, A., Aydın, R., ÖZDEMİR, V. F., & Yılmaz, A. (2021). Cattle and calf raising practices in the eastern Anatolia Region: An example of central county of Ağri province. International Journal of Agricultural and Natural Sciences, 14(3), 152–163. https://www.ijans.org/index.php/ijans/article/view/560
Kraft, M., Bernhardt, H., Brunsch, R., Büscher, W., Colangelo, E., Graf, H., Marquering, J., Tapken, H., Toppel, K., Westerkamp, C., & Ziron, M. (2022). Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study. Applied Sciences, 12(24), 12844. https://doi.org/10.3390/APP122412844 DOI: https://doi.org/10.3390/app122412844
Kustov, N. D., Дмитриевич, К. Н., Evdokimov, K. S., Сергеевич, Е. К., Shahmatov, A. V., & Владимирович, Ш. А. (2023). Space integrated network: architectural and technical solutions justifica-tion of the ReshUCube-2 space mission. Siberian Aerospace Journal, 24(2), 260–272. https://doi.org/10.31772/2712-8970-2023-24-2-260-272 DOI: https://doi.org/10.31772/2712-8970-2023-24-2-260-272
Lodkaew, T., Pasupa, K., & Loo, C. K. (2023). CowXNet: An automated cow estrus detection system. Expert Systems with Applications, 211, 118550. https://doi.org/10.1016/J.ESWA.2022.118550 DOI: https://doi.org/10.1016/j.eswa.2022.118550
López-Gatius, F. (2022). Revisiting the Timing of Insemination at Spontaneous Estrus in Dairy Cattle. Animals, 12(24), 3565. https://doi.org/10.3390/ANI12243565 DOI: https://doi.org/10.3390/ani12243565
Milford, A. B., Le Mouël, C., Bodirsky, B. L., & Rolinski, S. (2019). Drivers of meat consumption. Appetite, 141, 104313. https://doi.org/10.1016/J.APPET.2019.06.005 DOI: https://doi.org/10.1016/j.appet.2019.06.005
Ministerio de Desarrollo Agrario y Riego. (2024). Compendio anual de Producción Ganadera y Avícola. https://www.gob.pe/institucion/midagri/informes-publicaciones/2730346-compendio-anual-de-produccion-ganadera-y-avicola
Monteiro, A., Santos, S., & Gonçalves, P. (2021). Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals, 11(8), 2345. https://doi.org/10.3390/ANI11082345 DOI: https://doi.org/10.3390/ani11082345
Morgan-Davies, C., Lambe, N., Wishart, H., Waterhouse, T., Kenyon, F., McBean, D., & McCracken, D. (2018). Impacts of using a precision livestock system targeted approach in mountain sheep flocks. Livestock Science, 208, 67–76. https://doi.org/10.1016/J.LIVSCI.2017.12.002 DOI: https://doi.org/10.1016/j.livsci.2017.12.002
Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions. Sensors, 22(12), 4319. https://doi.org/10.3390/S22124319 DOI: https://doi.org/10.3390/s22124319
Nebel, R. L., Jones, C. M., & Roth, Z. (2011). Reproduction, Events and Management: Mating Management: Detection of Estrus. Encyclopedia of Dairy Sciences: Third edition, 1, 984–989. https://doi.org/10.1016/B978-0-12-818766-1.10116-3 DOI: https://doi.org/10.1016/B978-0-12-818766-1.10116-3
Niloofar, P., Francis, D. P., Lazarova-Molnar, S., Vulpe, A., Vochin, M. C., Suciu, G., Balanescu, M., Anestis, V., & Bartzanas, T. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, 190, 106406. https://doi.org/10.1016/J.COMPAG.2021.106406 DOI: https://doi.org/10.1016/j.compag.2021.106406
Noe, S. M., Zin, T. T., Tin, P., & Kobayashi, I. (2021). Automatic detection of mounting behavior in cattle using semantic segmentation and classification. LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies, 227–228. https://doi.org/10.1109/LIFETECH52111.2021.9391980 DOI: https://doi.org/10.1109/LifeTech52111.2021.9391980
Odintsov Vaintrub, M., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., & Vignola, G. (2021). Review: Precision livestock farming, automats and new technologies: possible applications in extensive dairy sheep farming. Animal, 15(3), 100143. https://doi.org/10.1016/J.ANIMAL.2020.100143 DOI: https://doi.org/10.1016/j.animal.2020.100143
Papakonstantinou, G. I., Voulgarakis, N., Terzidou, G., Fotos, L., Giamouri, E., & Papatsiros, V. G. (2024). Precision Livestock Farming Technology: Applications and Challenges of Animal Welfare and Climate Change. Agriculture, 14(4), 620. https://doi.org/10.3390/AGRICULTURE14040620 DOI: https://doi.org/10.3390/agriculture14040620
Perez Marquez, H. J., Ambrose, D. J., Schaefer, A. L., Cook, N. J., & Bench, C. J. (2019). Infrared thermography and behavioral biometrics associated with estrus indicators and ovulation in estrus-synchronized dairy cows housed in tiestalls. Journal of Dairy Science, 102(5), 4427–4440. https://doi.org/10.3168/JDS.2018-15221 DOI: https://doi.org/10.3168/jds.2018-15221
Pohler, K. G., Franco, G. A., Reese, S. T., & Smith, M. F. (2020). Physiology and pregnancy of beef cattle. Animal Agriculture: Sustainability, Challenges and Innovations, 37–55. https://doi.org/10.1016/B978-0-12-817052-6.00003-3 DOI: https://doi.org/10.1016/B978-0-12-817052-6.00003-3
Rahman, A., Smith, D. V., Little, B., Ingham, A. B., Greenwood, P. L., & Bishop-Hurley, G. J. (2018). Cattle behaviour classification from collar, halter, and ear tag sensors. Information Processing in Agriculture, 5(1), 124–133. https://doi.org/10.1016/J.INPA.2017.10.001 DOI: https://doi.org/10.1016/j.inpa.2017.10.001
Reith, S., & Hoy, S. (2018). Review: Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle. Animal, 12(2), 398–407. https://doi.org/10.1017/S1751731117001975 DOI: https://doi.org/10.1017/S1751731117001975
Remnant, J. G., Green, M. J., Huxley, J. N., & Hudson, C. D. (2018). Associations between dairy cow inter-service interval and probability of conception. Theriogenology, 114, 324–329. https://doi.org/10.1016/J.THERIOGENOLOGY.2018.03.029 DOI: https://doi.org/10.1016/j.theriogenology.2018.03.029
Riaz, U., Idris, M., Ahmed, M., Ali, F., & Yang, L. (2023). Infrared Thermography as a Potential Non-Invasive Tool for Estrus Detection in Cattle and Buffaloes. Animals, 13(8), 1425. https://doi.org/10.3390/ANI13081425 DOI: https://doi.org/10.3390/ani13081425
Röttgen, V., Becker, F., Tuchscherer, A., Wrenzycki, C., Düpjan, S., Schön, P. C., & Puppe, B. (2018). Vocalization as an indicator of estrus climax in Holstein heifers during natural estrus and superovulation. Journal of Dairy Science, 101(3), 2383–2394. https://doi.org/10.3168/JDS.2017-13412 DOI: https://doi.org/10.3168/jds.2017-13412
Scoones, I. (2023). Livestock, methane, and climate change: The politics of global assessments. Wiley Interdisciplinary Reviews: Climate Change, 14(1), e790. https://doi.org/10.1002/WCC.790 DOI: https://doi.org/10.1002/wcc.790
Shabani, I., Biba, T., & Çiço, B. (2022). Design of a Cattle-Health-Monitoring System Using Microservices and IoT Devices. Computersk, 11(5), 79. https://doi.org/10.3390/COMPUTERS11050079 DOI: https://doi.org/10.3390/computers11050079
Sharma, J., Tyagi, M., & Bhardwaj, A. (2021). Exploration of COVID-19 impact on the dimensions of food safety and security: a perspective of societal issues with relief measures. Journal of Agribusiness in Developing and Emerging Economies, 11(5), 452–471. https://doi.org/10.1108/JADEE-09-2020-0194/FULL/XML DOI: https://doi.org/10.1108/JADEE-09-2020-0194
Singh Rajput, A., Kumar Mohanty, T., Kumari Baithalu, R., Ahmed Mir, A., Lal, G. S., Singh Rajput, M., Kumar Dewery, R., Shah, N., Author Atul Singh Rajput, C., & Bhakat, M. (2022). Identification of estrus using infrared thermography in indigenous dairy animals. The Pharma Innovation Journal, 11(2S), 1571–1575. https://www.thepharmajournal.com/special-issue?year=2022&vol=11&issue=2S&ArticleId=11004
STMicroelectronics. (2024). STM32WLE5CC. https://www.st.com/en/microcontrollers-microprocessors/stm32wle5cc.html
Sun, Z., Yang, H., Liu, K., Yin, Z., Li, Z., & Xu, W. (2022). Recent Advances in LoRa: A Comprehensive Survey. ACM Transactions on Sensor Networks, 18(4). https://doi.org/10.1145/3543856 DOI: https://doi.org/10.1145/3543856
Tiwari, S., Singh, Y., Sirohi, R., Yadav, B., Singh, D. N., Gurung, A., & Shakya, P. (2021). Infrared thermographical differentiation of estrus and non-estrus stages of dairy animals. The Pharma Innovation Journal, 10(4S), 24–28. https://doi.org/10.22271/TPI.2021.V10.I4SA.5953 DOI: https://doi.org/10.22271/tpi.2021.v10.i4Sa.5953
United Nations Organization. (2023). Organización de las Naciones Unidas. United Nations. https://www.un.org/es/global-issues/population
Van Eerdenburg, F. J. C. M., Karthaus, D., Taverne, M. A. M., Merics, I., & Szenci, O. (2002). The Relationship between Estrous Behavioral Score and Time of Ovulation in Dairy Cattle. Journal of Dairy Science, 85(5), 1150–1156. https://doi.org/10.3168/JDS.S0022-0302(02)74177-5 DOI: https://doi.org/10.3168/jds.S0022-0302(02)74177-5
Vidal-Cardos, R., Fàbrega, E., & Dalmau, A. (2024). Determining calf traceability and cow–calf relationships in extensive farming using geolocation collars and BLE ear tags. Frontiers in Animal Science, 5, 1435729. https://doi.org/10.3389/FANIM.2024.1435729/BIBTEX DOI: https://doi.org/10.3389/fanim.2024.1435729
Wang, Z., Wang, S., Wang, C., Zhang, Y., Zong, Z., Wang, H., Su, L., & Du, Y. (2023). A Non-Contact Cow Estrus Monitoring Method Based on the Thermal Infrared Images of Cows. Agriculture, 13(2), 385. https://doi.org/10.3390/AGRICULTURE13020385 DOI: https://doi.org/10.3390/agriculture13020385
Wangler, A., Meyer, A., Rehbock, F., & Sanftleben, P. (2005). Wie effizient ist die Aktivitätsmessung als ein Hilfsmittel in der Brunsterkennung bei Milchrindern? Züchtungskunde, 77(3), 110–127.
Wróbel, B., Zielewicz, W., & Staniak, M. (2023). Challenges of Pasture Feeding Systems—Opportunities and Constraints. Agriculture, 13(5), 974. https://doi.org/10.3390/AGRICULTURE13050974 DOI: https://doi.org/10.3390/agriculture13050974
Yildiz, A. K., & Özgüven, M. M. (2022). Determination of Estrus in Cattle with Artificial Neural Networks Using Mobility and Environmental Data. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, 39(1), 40–45. https://doi.org/10.55507/GOPZFD.1116155 DOI: https://doi.org/10.55507/gopzfd.1116155
Zhao, X., Jiang, H., Guo, S., Liu, D., Liu, H., Shi, C., & Li, X. (2024). A Comprehensive Study of Open-Source Printed Circuit Board (PCB) Design Software Bugs. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2024.3450918 DOI: https://doi.org/10.1109/TIM.2024.3450918
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Copyright (c) 2025 Jaime Cesar Prieto-Luna, Aldo Alarcón-Sucasaca, Vadick Fernández-Romero, Yoen Hasmin Turpo-Galeano, Yesenia Rosy Delgado-Berrocal, Luis Alberto Holgado-Apaza
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