IMPLEMENTATION OF NEW TECHNOLOGIES IN AQUACULTURE
DOI:
https://doi.org/10.22267/revip.2181.27Keywords:
Artificial intelligence, sensor, algorithm, data modeling, RPAS (Remotely Piloted Aircraft System unmanned), ROV (Remotely Operated Vehicle).Abstract
Aquaculture is one of the fastest growing production systems worlds, which has propelled the development of new technologies has been promoted for optimising the production process and save operational costs in the industry. One of the tools implemented are artificial intelligence systems that allow producers to have a better knowledge about that happens underwater, this type of innovations within the aquaculture sector ensures the animal welfare of the fish and the efficiency of operations. Currently, hardware devices such as sensors are of relevance in measuring parameters and monitoring environmental conditions within the activity, made up of threenode systems; sensor node, coordinator node and publication node with a monitoring program that displays the values obtained and alerts when the specified reference limits are exceeded. In the tech market there are also remote sensors capable of locating and identifying productive marine areas, habitat characteristics, migration patterns and areas of fishing activity. There are also nanomaterials and nanotechnologies applied in analytical sciences, this implementation of biosensors in controlled environments and real environments allows characterizing the food behavior and health of organisms. Another innovative device that has joined the new era of aquaculture and technology are drones, multi-function, low-cost and unmanned devices, optimal for marine, fishing and aquaculture environments, capable of moving through large areas of culture collecting information and performing specific repair, control and monitoring tasks, thus counteracting labor costs.
Downloads
Download data is not yet available.
References
[1]. FAO. El estado mundial de la pesca y la acuicultura. 2020. Disponible en: https://www.fao.org/3/ca9229es/ca9229es.pdf
[2]. Gonzáles J, Nuñez B, Viloria P. Sistema de monitoreo en tiempo real para la medición de temperatura. Scientia et Technica. 2012, vol. 17, n.º 50, p. 128-131.
[3]. Olivo M, Verduzco J, García N, Villalobos J, Olivo A. Prototipo para el monitoreo automatizado de parámetros de calidad del agua en una granja de camarón. Científica. 2018, vol. 22, núm. 2, p. 87-95. Disponible en: https://www.redalyc.org/jatsRepo/614/61458109001/html/index.html
[4]. Navarro A, Prias J, Marin J, Padilla J. Construcción de un sistema de instrumentación para le medición de las variables que intervienen en la piscicultura bajo condiciones de estanque artificial. Armenia, Colombia. Universidad del Quindío. 2010.
[5]. Verduzco J, Figueroa P, Barajas J, Bricio E, Benavides J. Dispositivo portátil para monitoreo de calidad del agua en granjas acuícolas de camarón. Revista Ingeniantes. 2019. Disponible en: https://citt.itsm.edu.mx/ingeniantes/articulos/ingeniantes6no2vol2/8.%20Dispositivo%20Port%C3%A1til%20para%20Monitoreo%20de%20Par%C3%A1metros%20de%20Calidad%20del%20Agua%20en%20Granjas%20Acu%C3%ADcolas%20de%20Camar%C3%B3n.pdf
[6]. Hu Z, Li R, Xia X, Yu C, Fan X, Zhao Y. Environ Monit Assess. [Internet]. [consultado 12 septiembre 2021]. Disponible en: https://doi.org/10.1007/s10661-020-08409-9
[7]. Oviedo J, Oviedo A, Carmona C, Velez G, Reina J. Design of an aquaponic system monitored by internet of things and artificial intelligence. Revista espacios. 2020, vol. 41 (47), p. 60. Available from: http://www.revistaespacios.com/a20v41n47/a20v41n47p05.pdf
[8]. Yu R, He Y. Introduction to Machine Learning. Springer, Cham. In Deep Reinforcement Learning for Wireless Networks. 2019.
[9]. Baclic O, Tunis M, Young K, Doan C, Swerdfeger H, Schonfeld J. Natural language processing (NLP) a subfield of artificial intelligence. Government of Canada. 2020. Available from: https://www.canada.ca/en/public-health/services/reports-publications/canada-communicable-disease-report-ccdr/monthly-issue/2020-46/issue-6-june-4-2020/natural-language-processing-subfield-artificial-intelligence.html
[10]. Yigit E, Sabanci K, Toktas A, Kayabasi A. A study on visual features of leaves in plant identification using artificial intelligence techniques. ScienceDirect. 2019. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0168169918308524
[11]. Gao Z, Wanyama T, Singh I, Gadhrri A, Reiner S. From Industry 4.0 to Robotics 4.0-A Conceptual Framework for Collaborative and Intelligent Robotic Systems. ScienceDirect. 2020. Available from: https://www.sciencedirect.com/science/article/pii/S235197892030963X
[12]. Yue K, Yubang S. An overview of disruptive technologies for aquaculture. Aquaculture and Fisheries. 2021. Available from: https://doi.org/10.1016/j.aaf.2021.04.009
[13]. QuestionPro. Análisis de datos. [Internet]. 2019. [consultado 15 octubre 2021]. Disponible en: https://www.questionpro.com/es/analisis-de-datos.html
[14]. Gutirrez M, Ramirez J. Prototipo para el monitoreo automatizado de parámetros de calidad del agua en una granja de camarón. [Internet]. 2018. [consultado 12 noviembre 2021]. Disponible en: http://www.cientifica.esimez.ipn.mx/manuscritos/V22N2_087_095.pdf
[15]. Mwegoha W, Kaseva M, Sabai S. Mathematical modeling of dissolved oxygen in fish ponds. African Journal of Environmental Science and Technology. 2010, vol. 4, p. 623–638.
[16]. Aguilar J, Pacheco P, Rodrin S. “Automated aquaculture system that regulates pH, temperature and ammonia, IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). 2017. p. 1–6.
[17]. Chumkiew S, Jaroensutasinee M, Koad P. “Physical factors affecting oyster diversity and distribution in Southern Thailand,”. Journal of Environmental Biology. 2019. p. 03–08.
[18]. Rodriguez H, Anzola E. La calidad del agua y la productividad de un estanque en acuicultura. 2012. Disponible en: http://bibliotecadigital.agronet.gov.co/bitstream/11348/4997/3/051.3.pdf
[19]. Joong, K. K., Kyoung, J. P., Kyoung, S. C., Nam, S. W., Park, T. J., & Bajpai, R. (2005). Aerobic nitrification-denitrification by heterotrophic Bacillus strains. Bioresource Technology, 96(17), 1897–1906. https://doi.org/10.1016/j.biortech.2005.01.040
[20]. Metcalf-Eddy, Tchobanoglus, G., Stensel, H. D., Tsuchihashi, R., L, F., & Burton. (2013). Wasterwater Engineering Treatment and Reuse. (I. Metcalf & Eddy, Ed.) (fourth). New York: Mc Graw Hill.
[21]. Morita, M., Uemoto, H., & Watanabe, A. (2008). Nitrogen-removal bioreactor capable of simultaneous nitrification and denitrification for application to industrial wastewater treatment. Biochemical Engineering Journal, 41(1), 59–66. https://doi.org/10.1016/j.bej.2008.03.008
[22]. Gutierrez N. Agricultura y Desarrollo Rural. Calidad del agua en la acuicultura. [Internet]. 2014. [consultado 20 noviembre 2021]. Disponible en: https://sader.jalisco.gob.mx/fomento-acuicola-y-pesquero-e-inocuidad/519
[23]. Yang S, Li Y. “Dissolved oxygen remote monitoring system based on the internet,”. Electronic Measurement Technology, 2011. p. 88–90.
[24]. Duran J, Hernandez D. "Sistema hydroacústico para caracterizar el comportamiento alimentario de especies acuícola, Repositorio Universidad de Machala" [Internet]. 2017. [consultado 22 octubre 2021]. Disponible en: http://repositorio.utmachala.edu.ec/bitstream/48000/12472/1/T-2627_DURAN%20PESANTEZ%20JORDI%20MARCELO.pdf
[25]. IDEAM. Protocolo de monitoreo del agua. 2017. Disponible en: http://documentacion.ideam.gov.co/openbiblio/bvirtual/023773/PROTOCOLO_MONITOREO_AGUA_IDEAM.pdf
[26]. INVEMAR. Plan de investigaciones en sistemas de información geográfica –sig y sensores remotos –SR ajustado. Institución de Investigaciones Marinas y Costeras. INVEMAR; 2005. Disponible en: http://www.invemar.org.co/redcostera1/invemar/docs/2637PlanInvestigacionesSIG-SR2005-2010.pdf
[27]. Solanki H, Dwivedi S, Nayak V. Fishery forecast using OCM chlorophyll concentration and AVHRR SST. Remote Sens. 2003. p. 3691–3699.
[28]. Rodhouse P, Trathan P. Remote sensing of the global light-fishing fleet: an analysis of interactions with oceanography, other fisheries and predators. Adv. Mar. Biol. 2001. 261–303.
[29]. Valavanis V. Geographic Information Systems in oceanography and fisheries. Taylor & Francis. 2002. p. 240.
[30]. Agostini V, Bakun A. Ocean triads in the Mediterranean Sea: physical mechanisms potentially structuring reproductive habitat suitability (with example application to European anchovy, Engraulis encrasicolus). 2012; 11: 129–142.
[31]. Perez, A. Biosensores contra las enfermedades de peces y moluscos de acuicultura. Desarrollo Inteligente. 2010. Disponible en: https://canal.ugr.es/wp-content/uploads/2010/07/molusco.pdf
[32]. Holgado M. Aquicultura y pesca. Proyecto de Biosensor busca Prevenir Enfermedades en la Acuicultura: [Internet]. 2014. [consultado 29 noviembre 2021]. Disponible en: https://www.aqua.cl/2014/01/06/ue-proyecto-de-biosensor-busca-prevenir-enfermedades-en-la-acuicultura/#
[33]. Cheng K, Chan S, Lee H. Remote sensing of coastal algal blooms using unmanned aerial vehicles (UAVs). Marine Pollution Bulletin. 2020. Available from: https://doi.org/10.1016/j.marpolbul.2020.110889
[34]. Sousa D, Sargento S, Pereira A, Luis M. Self-adaptive Team of Aquatic Drones with a Communication Network for Aquaculture. EPIA conference on artificial intelligence. 2019. p. 569 - 580.
[35]. Yoo Seung-Hyeok, Ju Yeong-Tae, Kim Jong-Sil, Eung-Kon. Design and Development of Underwater Drone for Fish Farm Growth Environment Management. 2020 Available from: https://www.koreascience.or.kr/article/JAKO202030161610195.page
[36]. Hung-Yuan Chen, Shyi-Chyi Cheng, Chin-Chun Chang. Semantic scene modeling for aquaculture management using an autonomous drone. 2020. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of spie/11515/2566273/Semantic-scene-modeling-for-aquaculture-management-using-an-autonomous-drone/10.1117/12.2566273.short
[37]. Munguía A. Los drones vigilan el mar. [Internet]. 2018. [consultado 02 octubre 2021]. Disponible en: https://www.seg-social.es/wps/wcm/connect/wss/6ca8756e-d0cc-448c-94fb-bbc303111109/MAR585_marcadores.pdf?MOD=AJPERES
[38]. Marine Instruments. Tunadrone Uav. For the detection of free school tuna. 2021. Available from: https://www.marineinstruments.es/wp-content/uploads/2020/02/Tunadrone-av105-EN.pdf
[39]. ADENTU. Plashdrone 4, dron multifuncional a prueba de agua [Internet]. [consultado 20 octubre 2021]. Disponible en: https://www.adentu.cl/producto/splashdrone-4-dron-multifuncional-a-prueba-de-agua/
[40]. ESWELLPRO. El primer dron impermeable del mundo, ahora es aún mejor. [Internet]. [consultado 22 septiembre 2021]. Disponible en: https://eswellpro.com/splash-drone-3-plus/#impermeable
[41]. Jackson L. Ascenso de las máquinas: La revolución robótica en la acuacultura. Global Seafood Alliance. [Internet]. 2017. [consultado 02 septiembre 2021]. Disponible en: https://www.globalseafood.org/advocate/ascenso-de-las-maquinas-la-revolucion-robotica-en-la-acuacultura/
[42]. International Aquafeed. Cómo los ROV están Transformando la Acuicultura. 2020. Disponible en: https://aquafeed.co/entrada/como-los-rov-estan-transformando-la-acuicultura-21966
[43]. DJI. Drones con camára. [Internet]. [consultado 11 Febrero 2022]. Disponible en: https://store.dji.com/shop/education-and-industry?from=store-nav
[44]. Deep Trekker. Revolución Rov. [Internet]. [consultado 11 Febrero 2022]. Disponible en: https://www.deeptrekker.com/shop
[45]. Resolución No. 4201 del 27 de diciembre de 2018. Disponible en: https://www.aerocivil.gov.co/normatividad/Resoluciones%20TA%202018/RESL.%20%20N%C2%B0%2004201%20%20DIC%2027%20de%20%202018.pdf
[46]. Aquahoy. (14 de Junio de 2019). Avances en la nanotecnología para la acuicultura y pesca sostenible. Disponible en: https://www.aquahoy.com/i-d-i/sistemas-de-cultivo/33389-avances-en-la-nanotecnologia-para-la-acuicultura-y-pesca-sostenible
[47]. Martos Sitcha, J., Sosa, J., Valido, D., Bravo, F., Calduch Giner, J., Cabruja, E., . . . Perez Sanchez, J. (29 de Mayo de 2019). Ultra-Low Power Sensor Devices for Monitoring Physical Activity and Respiratory Frequency in Farmed Fish. Available from: https://doi.org/10.3389/fphys.2019.00667
[48]. Wang, J., Zhao, J., Wang, Y., Wang, W., Gao, Y., Xu, R., & Zhao, W. A New Microfluidic Device for Classification of Microalgae Cells Based on Simultaneous Analysis of Chlorophyll Fluorescence, Side Light Scattering, Resistance Pulse Sensing. Micromachines. 2006. Available from: https://doi.org/10.3389/fphys.2019.00667
[49]. Su X, Sutarlie L, Jun X. Sensors, Biosensors, and Analytical Technologies for Aquaculture Water Quality. 2019, Article ID 8272705, p. 15.
[50]. Piamba T, Zambrano L, Montaño L. Implementación de un sistema de monitoreo IoT aplicado a una piscicultura de trucha. Sena. 2020. Disponible en: http://revistas.sena.edu.co/index.php/inf_tec/article/view/2937/3686
[2]. Gonzáles J, Nuñez B, Viloria P. Sistema de monitoreo en tiempo real para la medición de temperatura. Scientia et Technica. 2012, vol. 17, n.º 50, p. 128-131.
[3]. Olivo M, Verduzco J, García N, Villalobos J, Olivo A. Prototipo para el monitoreo automatizado de parámetros de calidad del agua en una granja de camarón. Científica. 2018, vol. 22, núm. 2, p. 87-95. Disponible en: https://www.redalyc.org/jatsRepo/614/61458109001/html/index.html
[4]. Navarro A, Prias J, Marin J, Padilla J. Construcción de un sistema de instrumentación para le medición de las variables que intervienen en la piscicultura bajo condiciones de estanque artificial. Armenia, Colombia. Universidad del Quindío. 2010.
[5]. Verduzco J, Figueroa P, Barajas J, Bricio E, Benavides J. Dispositivo portátil para monitoreo de calidad del agua en granjas acuícolas de camarón. Revista Ingeniantes. 2019. Disponible en: https://citt.itsm.edu.mx/ingeniantes/articulos/ingeniantes6no2vol2/8.%20Dispositivo%20Port%C3%A1til%20para%20Monitoreo%20de%20Par%C3%A1metros%20de%20Calidad%20del%20Agua%20en%20Granjas%20Acu%C3%ADcolas%20de%20Camar%C3%B3n.pdf
[6]. Hu Z, Li R, Xia X, Yu C, Fan X, Zhao Y. Environ Monit Assess. [Internet]. [consultado 12 septiembre 2021]. Disponible en: https://doi.org/10.1007/s10661-020-08409-9
[7]. Oviedo J, Oviedo A, Carmona C, Velez G, Reina J. Design of an aquaponic system monitored by internet of things and artificial intelligence. Revista espacios. 2020, vol. 41 (47), p. 60. Available from: http://www.revistaespacios.com/a20v41n47/a20v41n47p05.pdf
[8]. Yu R, He Y. Introduction to Machine Learning. Springer, Cham. In Deep Reinforcement Learning for Wireless Networks. 2019.
[9]. Baclic O, Tunis M, Young K, Doan C, Swerdfeger H, Schonfeld J. Natural language processing (NLP) a subfield of artificial intelligence. Government of Canada. 2020. Available from: https://www.canada.ca/en/public-health/services/reports-publications/canada-communicable-disease-report-ccdr/monthly-issue/2020-46/issue-6-june-4-2020/natural-language-processing-subfield-artificial-intelligence.html
[10]. Yigit E, Sabanci K, Toktas A, Kayabasi A. A study on visual features of leaves in plant identification using artificial intelligence techniques. ScienceDirect. 2019. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0168169918308524
[11]. Gao Z, Wanyama T, Singh I, Gadhrri A, Reiner S. From Industry 4.0 to Robotics 4.0-A Conceptual Framework for Collaborative and Intelligent Robotic Systems. ScienceDirect. 2020. Available from: https://www.sciencedirect.com/science/article/pii/S235197892030963X
[12]. Yue K, Yubang S. An overview of disruptive technologies for aquaculture. Aquaculture and Fisheries. 2021. Available from: https://doi.org/10.1016/j.aaf.2021.04.009
[13]. QuestionPro. Análisis de datos. [Internet]. 2019. [consultado 15 octubre 2021]. Disponible en: https://www.questionpro.com/es/analisis-de-datos.html
[14]. Gutirrez M, Ramirez J. Prototipo para el monitoreo automatizado de parámetros de calidad del agua en una granja de camarón. [Internet]. 2018. [consultado 12 noviembre 2021]. Disponible en: http://www.cientifica.esimez.ipn.mx/manuscritos/V22N2_087_095.pdf
[15]. Mwegoha W, Kaseva M, Sabai S. Mathematical modeling of dissolved oxygen in fish ponds. African Journal of Environmental Science and Technology. 2010, vol. 4, p. 623–638.
[16]. Aguilar J, Pacheco P, Rodrin S. “Automated aquaculture system that regulates pH, temperature and ammonia, IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). 2017. p. 1–6.
[17]. Chumkiew S, Jaroensutasinee M, Koad P. “Physical factors affecting oyster diversity and distribution in Southern Thailand,”. Journal of Environmental Biology. 2019. p. 03–08.
[18]. Rodriguez H, Anzola E. La calidad del agua y la productividad de un estanque en acuicultura. 2012. Disponible en: http://bibliotecadigital.agronet.gov.co/bitstream/11348/4997/3/051.3.pdf
[19]. Joong, K. K., Kyoung, J. P., Kyoung, S. C., Nam, S. W., Park, T. J., & Bajpai, R. (2005). Aerobic nitrification-denitrification by heterotrophic Bacillus strains. Bioresource Technology, 96(17), 1897–1906. https://doi.org/10.1016/j.biortech.2005.01.040
[20]. Metcalf-Eddy, Tchobanoglus, G., Stensel, H. D., Tsuchihashi, R., L, F., & Burton. (2013). Wasterwater Engineering Treatment and Reuse. (I. Metcalf & Eddy, Ed.) (fourth). New York: Mc Graw Hill.
[21]. Morita, M., Uemoto, H., & Watanabe, A. (2008). Nitrogen-removal bioreactor capable of simultaneous nitrification and denitrification for application to industrial wastewater treatment. Biochemical Engineering Journal, 41(1), 59–66. https://doi.org/10.1016/j.bej.2008.03.008
[22]. Gutierrez N. Agricultura y Desarrollo Rural. Calidad del agua en la acuicultura. [Internet]. 2014. [consultado 20 noviembre 2021]. Disponible en: https://sader.jalisco.gob.mx/fomento-acuicola-y-pesquero-e-inocuidad/519
[23]. Yang S, Li Y. “Dissolved oxygen remote monitoring system based on the internet,”. Electronic Measurement Technology, 2011. p. 88–90.
[24]. Duran J, Hernandez D. "Sistema hydroacústico para caracterizar el comportamiento alimentario de especies acuícola, Repositorio Universidad de Machala" [Internet]. 2017. [consultado 22 octubre 2021]. Disponible en: http://repositorio.utmachala.edu.ec/bitstream/48000/12472/1/T-2627_DURAN%20PESANTEZ%20JORDI%20MARCELO.pdf
[25]. IDEAM. Protocolo de monitoreo del agua. 2017. Disponible en: http://documentacion.ideam.gov.co/openbiblio/bvirtual/023773/PROTOCOLO_MONITOREO_AGUA_IDEAM.pdf
[26]. INVEMAR. Plan de investigaciones en sistemas de información geográfica –sig y sensores remotos –SR ajustado. Institución de Investigaciones Marinas y Costeras. INVEMAR; 2005. Disponible en: http://www.invemar.org.co/redcostera1/invemar/docs/2637PlanInvestigacionesSIG-SR2005-2010.pdf
[27]. Solanki H, Dwivedi S, Nayak V. Fishery forecast using OCM chlorophyll concentration and AVHRR SST. Remote Sens. 2003. p. 3691–3699.
[28]. Rodhouse P, Trathan P. Remote sensing of the global light-fishing fleet: an analysis of interactions with oceanography, other fisheries and predators. Adv. Mar. Biol. 2001. 261–303.
[29]. Valavanis V. Geographic Information Systems in oceanography and fisheries. Taylor & Francis. 2002. p. 240.
[30]. Agostini V, Bakun A. Ocean triads in the Mediterranean Sea: physical mechanisms potentially structuring reproductive habitat suitability (with example application to European anchovy, Engraulis encrasicolus). 2012; 11: 129–142.
[31]. Perez, A. Biosensores contra las enfermedades de peces y moluscos de acuicultura. Desarrollo Inteligente. 2010. Disponible en: https://canal.ugr.es/wp-content/uploads/2010/07/molusco.pdf
[32]. Holgado M. Aquicultura y pesca. Proyecto de Biosensor busca Prevenir Enfermedades en la Acuicultura: [Internet]. 2014. [consultado 29 noviembre 2021]. Disponible en: https://www.aqua.cl/2014/01/06/ue-proyecto-de-biosensor-busca-prevenir-enfermedades-en-la-acuicultura/#
[33]. Cheng K, Chan S, Lee H. Remote sensing of coastal algal blooms using unmanned aerial vehicles (UAVs). Marine Pollution Bulletin. 2020. Available from: https://doi.org/10.1016/j.marpolbul.2020.110889
[34]. Sousa D, Sargento S, Pereira A, Luis M. Self-adaptive Team of Aquatic Drones with a Communication Network for Aquaculture. EPIA conference on artificial intelligence. 2019. p. 569 - 580.
[35]. Yoo Seung-Hyeok, Ju Yeong-Tae, Kim Jong-Sil, Eung-Kon. Design and Development of Underwater Drone for Fish Farm Growth Environment Management. 2020 Available from: https://www.koreascience.or.kr/article/JAKO202030161610195.page
[36]. Hung-Yuan Chen, Shyi-Chyi Cheng, Chin-Chun Chang. Semantic scene modeling for aquaculture management using an autonomous drone. 2020. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of spie/11515/2566273/Semantic-scene-modeling-for-aquaculture-management-using-an-autonomous-drone/10.1117/12.2566273.short
[37]. Munguía A. Los drones vigilan el mar. [Internet]. 2018. [consultado 02 octubre 2021]. Disponible en: https://www.seg-social.es/wps/wcm/connect/wss/6ca8756e-d0cc-448c-94fb-bbc303111109/MAR585_marcadores.pdf?MOD=AJPERES
[38]. Marine Instruments. Tunadrone Uav. For the detection of free school tuna. 2021. Available from: https://www.marineinstruments.es/wp-content/uploads/2020/02/Tunadrone-av105-EN.pdf
[39]. ADENTU. Plashdrone 4, dron multifuncional a prueba de agua [Internet]. [consultado 20 octubre 2021]. Disponible en: https://www.adentu.cl/producto/splashdrone-4-dron-multifuncional-a-prueba-de-agua/
[40]. ESWELLPRO. El primer dron impermeable del mundo, ahora es aún mejor. [Internet]. [consultado 22 septiembre 2021]. Disponible en: https://eswellpro.com/splash-drone-3-plus/#impermeable
[41]. Jackson L. Ascenso de las máquinas: La revolución robótica en la acuacultura. Global Seafood Alliance. [Internet]. 2017. [consultado 02 septiembre 2021]. Disponible en: https://www.globalseafood.org/advocate/ascenso-de-las-maquinas-la-revolucion-robotica-en-la-acuacultura/
[42]. International Aquafeed. Cómo los ROV están Transformando la Acuicultura. 2020. Disponible en: https://aquafeed.co/entrada/como-los-rov-estan-transformando-la-acuicultura-21966
[43]. DJI. Drones con camára. [Internet]. [consultado 11 Febrero 2022]. Disponible en: https://store.dji.com/shop/education-and-industry?from=store-nav
[44]. Deep Trekker. Revolución Rov. [Internet]. [consultado 11 Febrero 2022]. Disponible en: https://www.deeptrekker.com/shop
[45]. Resolución No. 4201 del 27 de diciembre de 2018. Disponible en: https://www.aerocivil.gov.co/normatividad/Resoluciones%20TA%202018/RESL.%20%20N%C2%B0%2004201%20%20DIC%2027%20de%20%202018.pdf
[46]. Aquahoy. (14 de Junio de 2019). Avances en la nanotecnología para la acuicultura y pesca sostenible. Disponible en: https://www.aquahoy.com/i-d-i/sistemas-de-cultivo/33389-avances-en-la-nanotecnologia-para-la-acuicultura-y-pesca-sostenible
[47]. Martos Sitcha, J., Sosa, J., Valido, D., Bravo, F., Calduch Giner, J., Cabruja, E., . . . Perez Sanchez, J. (29 de Mayo de 2019). Ultra-Low Power Sensor Devices for Monitoring Physical Activity and Respiratory Frequency in Farmed Fish. Available from: https://doi.org/10.3389/fphys.2019.00667
[48]. Wang, J., Zhao, J., Wang, Y., Wang, W., Gao, Y., Xu, R., & Zhao, W. A New Microfluidic Device for Classification of Microalgae Cells Based on Simultaneous Analysis of Chlorophyll Fluorescence, Side Light Scattering, Resistance Pulse Sensing. Micromachines. 2006. Available from: https://doi.org/10.3389/fphys.2019.00667
[49]. Su X, Sutarlie L, Jun X. Sensors, Biosensors, and Analytical Technologies for Aquaculture Water Quality. 2019, Article ID 8272705, p. 15.
[50]. Piamba T, Zambrano L, Montaño L. Implementación de un sistema de monitoreo IoT aplicado a una piscicultura de trucha. Sena. 2020. Disponible en: http://revistas.sena.edu.co/index.php/inf_tec/article/view/2937/3686
Downloads
Published
2022-06-24
Issue
Section
Revisión Literaria
License
La plataforma OJS para envío de manuscritos le ha solicitado que marque las casillas correspondientes a la Declaración de Derechos de Autor, autorizando a Investigación Pecuaria para que el artículo pueda ser publicado. El autor principal asume la responsabilidad de los coautores para tal fin. El licenciamiento (by-nc) se enmarca dentro de “Creative Commons” (http://creativecommons.org/licenses/by-nc/4.0/deed.es_ES). Se entiende que todos los documentos, tablas y figuras son originales y autorizadas por los autores respectivos para su publicación.
