IoT na Captura e Análise de Som na Agroindústria: Um Estudo de Literatura

Autores

DOI:

https://doi.org/10.47633/81qth861

Palavras-chave:

Internet das Coisas (IoT), análise de som na agroindústria, monitoramento acústico na agricultura, detecção de pragas via IoT, agricultura inteligente

Resumo

Este artigo tem como objetivo revisar e analisar a literatura sobre a aplicação da Internet das Coisas (IoT) na captura e análise de som na agroindústria, uma área emergente com grande potencial para melhorar a eficiência e sustentabilidade agrícola. O estudo busca identificar o estado atual da pesquisa, as principais tendências e áreas de oportunidade nesse campo. Uma revisão sistemática da literatura foi realizada utilizando o Google Acadêmico como base de dados, com foco em publicações que abordam especificamente a integração do IoT no monitoramento acústico. O processo incluiu a coleta e análise de dados bibliométricos, além da avaliação das características das pesquisas realizadas até o momento. Os resultados destacam que a maioria dos estudos é recente, com foco particular em aplicações como a detecção de pragas e o monitoramento de culturas por meio da análise de som. Apesar do crescente interesse, foram identificadas lacunas significativas em áreas como o uso de inteligência artificial e aprendizado de máquina na interpretação dos dados acústicos coletados. O estudo conclui que é essencial continuar desenvolvendo sistemas IoT mais avançados, especialmente em campos como a apicultura e a ecologia, onde a aplicação da análise de som pode ter um impacto significativo. Este trabalho oferece uma base sólida para pesquisas futuras e o desenvolvimento de tecnologias inovadoras na agroindústria.

Downloads

Os dados de download ainda não estão disponíveis.

Biografia do Autor

  • Andrés Antonio Calvo Vargas, Universidad Nacional. Heredia, Costa Rica

    Informático

    Estudiante de la Universidad Nacional

  • Camilo José González Fuentes, Universidad Nacional. Heredia, Costa Rica

    Informático

    Estudiante de Universidad Nacional

  • Gorki Iván Romero Valerio, Universidad Nacional. Heredia, Costa Rica

    Informático

    Estudiante de la Universidad Nacional

  • Sergio Alonso Romero Valverde, Universidad Nacional. Heredia, Costa Rica

    Informático

    Estudiante de la Universidad Nacional

  • Fulvio Lizano Madriz, Universidad Nacional. Heredia, Costa Rica

    Máster en Apicultura Tropical (CINAT).

    Máster en Ciencias de la Computación (TEC).

    Doctor en Ciencias de la Computación e Ingeniería (Aalborg University)

Referências

Referencias

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. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2020.3047818

Abdollahi, M., Giovenazzo, P., & Falk, T. H. (2022). Automated beehive acoustics monitoring: A comprehensive review of the literature and recommendations for future work. Applied Sciences, 12(8), 3920. https://doi.org/10.3390/app12083920

Adesipo, A., Fadeyi, O., Kuca, K., Krejcar, O., Maresova, P., Selamat, A., & Adenola, M. (2020). Smart and climate-smart agricultural trends as core aspects of smart village functions. Sensors, 20(21), 5977. https://doi.org/10.3390/s20215977

Ajao, L., Adedokun, E. A., Mua'zu, M. B., & Agajo, J. (2021). Smart embedded wireless system design: An Internet of Things realization. International Journal of Automation and Smart Technology, 11(1), Article 2146. https://doi.org/10.5875/ausmt.v11i1.2146

Ali, M. A., Sharma, A. K., & Dhanaraj, R. K. (2024). Heterogeneous features and deep learning networks fusion-based pest detection, prevention and controlling system using IoT and pest sound analytics in a vast agriculture system. Computers and Electrical Engineering, 116, 109146. https://doi.org/10.1016/j.compeleceng.2024.109146

Astill, J., Dara, R. A., Fraser, E. D. G., & Sharif, S. (2018). Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data: A focus on avian influenza virus. Frontiers in Veterinary Science, 5. https://doi.org/10.3389/fvets.2018.00263

Bankinter & Accenture. (2011). El internet de las cosas. Fundación de la Innovación Bankinter. Recuperado el 16 de junio de 2024 de https://www.fundacionbankinter.org/wp-content/uploads/2021/09/Publicacion-PDF-ES-FTF_IOT.pdf

Camargo Jáuregui, W. H. (2016). Las TIC y su aplicación en la captura y análisis de datos relacionados con el ruido ambiental en el contexto físico de la Universidad Francisco de Paula Santander. Maestría en Desarrollo Sostenible y Medioambiente. Recuperado de https://ridum.umanizales.edu.co/xmlui/handle/20.500.12746/3082

Chen, G., Li, C., Guo, Y., Shu, H., Cao, Z., & Xu, B. (2022). Recognition of cattle’s feeding behaviors using noseband pressure sensor with machine learning. Frontiers in Veterinary Science, 9:822621. https://doi.org/10.3389/fvets.2022.822621

Espinoza Ortiz, C. E., & Sandoval Sandoval, E. G. (2022). Protocolo para telemetría por medio de la tecnología celular GSM y SMS empleando el microcontrolador Arduino. Repositorio Institucional - UCV. https://repositorio.ucv.edu.pe/handle/20.500.12692/113803

Guntoro, B., Hoang, Q. N., A’yun, A. Q., & Rochijan. (2019). Dynamic responses of livestock farmers to smart farming. IOP Conference Series: Earth and Environmental Science, 372(1), 012042. https://doi.org/10.1088/1755-1315/372/1/012042

Hoye, T., August, T., Balzan, M., & Biesmeijer, K. (2023). Modern approaches to the monitoring of biodiversity (MAMBO). RIO Journal of Research and Innovation, 10(1). https://doi.org/10.3897/rio.9.e116951

Iannace, G., Trematerra, A., & Lombardi, I. (2021). Effects of nightlife noise in a city center. Noise Mapping, 8(1), 228–235. https://doi.org/10.1515/noise-2021-0018

Imoize, A. L., Odeyemi, S. D., & Adebisi, J. A. (2020). Development of a low-cost wireless bee-hive temperature and sound monitoring system. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 8(3), Article 3. https://doi.org/10.52549/ijeei.v8i3.2268

Juárez, R. (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

Karar, M., Reyad, O., Abdel-Aty, A.-H., Owyed, S., & Hassan, M. (2021). Intelligent IoT-aided early sound detection of red palm weevils. Computers, Materials & Continua, 69(3), 4095–4111. https://doi.org/10.32604/cmc.2021.019059

Kleanthous, N., Hussain, A., Khan, W., Sneddon, J., & Liatsis, P. (2022). Deep transfer learning in sheep activity recognition using accelerometer data. Expert Systems with Applications, 207, 117925. https://doi.org/10.1016/j.eswa.2022.117925

Klotz, D. F., Ribeiro, R., Enembreck, F., Denardin, G., Barbosa, M., Casanova, D., & Teixeira, M. (2020, agosto 17). Estimating action plans for smart poultry houses. arXiv. https://doi.org/10.48550/arXiv.2008.07356

Krishnan, S., Prasanth, N., Ralphin, J. B., & Rajalakshmi, N. (2022). Cloud IoT systems for smart agricultural engineering. Routledge & CRC Press. https://doi.org/10.1201/9781003185413

Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of things (IoT): A literature review. Journal of Computer and Communications, 3(5), 164-173. https://doi.org/10.4236/jcc.2015.35021

Mahfuz, S., Mun, H.-S., Dilawar, M., & Yang, C.-J. (2022). Applications of smart technology as a sustainable strategy in modern swine farming. Sustainability, 14(5), 2607. https://doi.org/10.3390/su14052607

Márquez Guerrero, C. S. (2019). Internet de las cosas aplicado al sector avícola de Santander (Colombia): Prototipo orientado a una empresa del área metropolitana de Bucaramanga. Universidad Autónoma de Bucaramanga. https://repository.unab.edu.co/handle/20.500.12749/7276

Martinez-Rau, L. S., Chelotti, J. O., Ferrero, M., Galli, J. R., Utsumi, S. A., Planisich, A. M., Rufiner, H. L., & Giovanini, L. L. (2023, agosto 28). A noise-robust acoustic method for recognizing foraging activities of grazing cattle. arXiv. https://doi.org/10.48550/arXiv.2304.14824

Martinez-Rau, L. S., Chelotti, J. O., Ferrero, M., Utsumi, S. A., Planisich, A. M., Vignolo, L. D., Giovanini, L. L., Rufiner, H. L., & Galli, J. R. (2023). Daylong acoustic recordings of grazing and rumination activities in dairy cows. Scientific Data, 10(1), 782. https://doi.org/10.1038/s41597-023-02673-3

Mazon-Olivo, B., & Pan, A. (2022). Internet de las cosas: Estado del arte, paradigmas computacionales y arquitecturas de referencia. IEEE Latin America Transactions, 20(1), Article 1. https://doi.org/10.1109/TLA.2022.9662173

Monta, C., Ayala, P., Cáceres, J., García, C. A., & García, M. (2020). Control difuso de bajo costo para sistemas de calefacción en avícolas. Revista Ibérica de Sistemas e Tecnologias de Informação, N.º E37, 180-193. https://www.proquest.com/openview/af4853282a08b4be33c2bfeb4d8ca239/1?pq-origsite=gscholar&cbl=1006393

Morone, 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

Mrozek, D., Górny, R., Wachowicz, A., & Małysiak-Mrozek, B. (2021). Edge-based detection of varroosis in beehives with IoT devices with embedded and TPU-accelerated machine learning. Applied Sciences, 11(22), 11078. https://doi.org/10.3390/app112211078

Mulla Suquisupa, C. A. (2023). Industria 4.0: Inmersión de las empresas españolas en la cuarta revolución industrial. Caso de estudio: Gestamp Smart Factory [Trabajo de fin de grado, Universidad de Valladolid]. UVaDoc. https://uvadoc.uva.es/handle/10324/63614

Neethirajan, S. (2020). Digitalization of Animal Farming. https://doi.org/10.20944/preprints202007.0040.v1

Neethirajan, S. (2022). Affective state recognition in livestock—Artificial intelligence approaches Animals, 12(6), 759. https://doi.org/10.3390/ani12060759

Neethirajan, S., & Kemp, B. (2021). Digital livestock farming. Sensing and Bio-Sensing Research, 32, 100408. https://doi.org/10.1016/j.sbsr.2021.100408

Ngo, H. Q. T., Nguyen, T. P., & Nguyen, H. (2020). Research on a low-cost, open-source, and remote monitoring data collector to predict livestock’s habits based on location and auditory information: A case study from Vietnam. Agriculture, 10(5), 180. https://doi.org/10.3390/agriculture10050180

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: An International Journal of Animal Bioscience, 15(3), 100143. https://doi.org/10.1016/j.animal.2020.100143

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

Pavlíčková, M., Mojžišová, A., & Pócsová, J. (2022). Hoshin Kanri process: A review and bibliometric analysis on the connection of theory and practice. Processes, 10(9), 1854. https://doi.org/10.3390/pr10091854

Prats Rosa, J. (2022). Estudio de una aplicación IoT para diagnóstico de sistemas industriales [Trabajo final de grado, Universitat Politècnica de Catalunya]. UPCommons. https://upcommons.upc.edu/handle/2117/380432

Radhika, R., Shobana, M., Balasaraswathi, V. R., Fancy, C., & Shamala, L. M. (2022). An automated irrigation system with movement and sound detection sensor for crop shielding. In 2022 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 38–44). IEEE. https://doi.org/10.1109/ICCES54183.2022.9835897

Raghu, R., Jayaramaan, V., Jayaraman, J., Nukala, S. S. V., & Montenegro-Marin, C. E. (2022). A user-centered security approach to create an IoT-based multi-layered fog-cloud architecture for data optimization in raised bed farming. International Journal of Safety and Security Engineering, 12(6), 767–776. https://doi.org/10.18280/ijsse.120614

Ritchie, S. M., Young, L. M., & Sigman, J. (2018). Comparison of selected bibliographic database subject overlap for agricultural information. Issues in Science and Technology Librarianship, (89). https://doi.org/10.29173/istl1727

Romaneo, J. (2017). The current global situation and challenges of RPW management programs. En Proceedings of the Scientific Consultation and High-Level Meeting on Red Palm Weevil Management (pp. 85–95). FAO. https://openknowledge.fao.org/server/api/core/bitstreams/8b2c1cb2-1856-47ec-b072-6b74bd38abde/content#page=93

Rotaru, A., Vâtcă, A., Pop, I., & Andronie, L. (2022). Artificial intelligence, a possible solution for agriculture and animal husbandry sector? Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies, 78(2). https://doi.org/10.15835/buasvmcn-asb:2021.0004

Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., Reed, M., & Fraser, E. D. G. (2019). The politics of digital agricultural technologies: A preliminary review. Sociologia Ruralis, 59(2). https://doi.org/10.1111/soru.12233

Schuller, B. W., Akman, A., Chang, Y., Coppock, H., Gebhard, A., Kathan, A., Rituerto-González, E., Triantafyllopoulos, A., & Pokorny, F. B. (2024). Ecology & computer audition: Applications of audio technology to monitor organisms and environment. Heliyon, 10(1), e23142. https://doi.org/10.1016/j.heliyon.2023.e23142

Spanaki, K., Sivarajah, U., Fakhimi, M., Despoudi, S., & Irani, Z. (2022). Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research, 308(1), 491–524. https://doi.org/10.1007/s10479-020-03922-z

Trujillo Burbano, I. A. (2022). Diseño de una red LoRa que permita la interconexión de sensores inalámbricos en la ciudad de Riobamba bajo el concepto de smart cities [Trabajo de titulación, Escuela Superior Politécnica de Chimborazo]. DSpace ESPOCH. http://dspace.espoch.edu.ec/handle/123456789/20831

Williamson, K., & Johanson, G. (Eds.). (2017). Research methods: Information, systems, and contexts (2ª ed.). Chandos Publishing. https://books.google.co.cr/books?id=GVPXDgAAQBAJ

Zheng, H., Zhang, T., Fang, C., Zeng, J., & Yang, X. (2021). Design and implementation of poultry farming information management system based on cloud database. Animals, 11(3), 900. https://doi.org/10.3390/ani11030900

Publicado

2025-11-03

Edição

Seção

Artículos (sección arbitrada)

Como Citar

IoT na Captura e Análise de Som na Agroindústria: Um Estudo de Literatura. (2025). Revista Agro, 3(1), 1-25. https://doi.org/10.47633/81qth861

Artigos Semelhantes

1-10 de 24

Você também pode iniciar uma pesquisa avançada por similaridade para este artigo.