Aplicación de drones en la agricultura internacional y cubana.

Contenido principal del artículo

María Elena Ruiz Pérez
Roberto García Reyes
Neili Machado García

Resumen

La introducción de diferentes tecnologías como los Sistemas de Información Geográfica, las imágenes obtenidas a partir de satélites, aviones, drones, los diversos tipos de sensores, así como los Sistemas y herramientas informáticas han provocado una revolución en la Agricultura. La utilización de estas tecnologías siempre ha tenido el interés de emplear de una forma eficaz y eficiente los recursos disponibles, así como humanizar el trabajo agrícola. En este artículo se hace una revisión de los beneficios obtenidos con el empleo de los drones a nivel internacional y también en Cuba. No obstante, se han citado también los trabajos que plantean la utilización de la Agricultura de Precisión en Cuba. Se observa que todavía son pocos los trabajos publicados que describan de forma detallada los resultados obtenidos que permitan su reproducibilidad y abundan los que describen los mismos de forma cualitativa. Se considera que en el caso particular de los drones, resulta muy costosa todavía la extensión de su utilización ya que de forma práctica sólo la Empresa GEOCUBA dispone de toda la infraestructura y el personal capacitado para su utilización más completa por lo que las diferentes empresas, instituciones y campesinos que deseen emplearlos deben realizar grandes desembolsos.

Detalles del artículo

Cómo citar
Ruiz Pérez, M. E., García Reyes, R., & Machado García, N. (2024). Aplicación de drones en la agricultura internacional y cubana . Revista Ciencias Técnicas Agropecuarias, 33(1), https://cu-id.com/2177/v33n1e07. Recuperado a partir de https://revistas.unah.edu.cu/index.php/rcta/article/view/1845
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ACHARYA, B.S.; BHANDARI, M.; BANDINI, F.; PIZARRO, A.C.E.; PERKS, M.; JOSHI, D.R.; WANG, S.; DOGWILER, T.; RAY, R.L.; KHAREL, G.: “Unmanned aerial vehicles in hydrology and water management: Applications, challenges, and perspectives”, Water Resources Research, 57(11), 2021, ISSN: 0043-1397.

AHIRWAR, S.; SWARNKAR, R.; BHUKYA, S.; NAMWADE, G.: “Application of drone in agriculture”, International Journal of Current Microbiology and Applied Sciences, 8(01): 2500-2505, 2019, DOI: https://doi.org/10.20546/ijcmas.2019.801.264.

ALMEIDA-MALDONADO, E.; CAMEJO-BARREIRO, L.E.; SANTIESTEBAN-TOCA, C.E.: “La fertirrigación inteligente, pilar de una agricultura sostenible”, Revista Cubana de Ciencias Informáticas, 11(3): 36-49, 2017, ISSN: 2227-1899.

BACCO, M.; BERTON, A.; FERRO, E.; GENNARO, C.; GOTTA, A.; MATTEOLI, S.; PAONESSA, F.; RUGGER, M.; VIRONE, G.; ZANELLA, A.: “vSmart farming: Opportunities, challenges and technology enablers. 2018 IoT Vertical and Topical Summit on Agriculture -Tuscany”, IOT Tuscany, : 1-6, 2018, DOI: https://doi.org/10.1109/IOTTUSCANY.2018.8373043.

BENDIG, J.; BOLTEN, A.; BENNERTZ, S.; BROSCHEIT, J.; EICHFUSS, S.; BARETH, G.: “Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging”, Remote sensing, 6(11): 10395-10412, 2014, ISSN: 2072-4292.

BREWSTER, C.; ROUSSAKI, I.; KALATZIS, N.; FUKAMI, K.; ELLIS, K.: “IoT in agriculture: Designing a Europe-wide large-scale pilot”, IEEE communications magazine, 55(9): 26-33, 2017, ISSN: 0163-6804.

DAWALIBY, S.; ABERKANE, A.; BRADAI, A.: “Blockchain-based IoT platform for autonomous drone operations management”, En: Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and beyond, pp. 31-36, 2020, DOI: . https://doi.org/10.1145/3414045.3415939.

DENG, L.; MAO, Z.; LI, X.; HU, Z.; DUAN, F.; YAN, Y.: “UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras”, ISPRS journal of photogrammetry and remote sensing, 146: 124-136, 2018, ISSN: 0924-2716.

ELIJAH, O.; RAHMAN, T.A.; ORIKUMHI, I.; LEOW, C.Y.; HINDIA, M.N.: “An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges”, IEEE Internet of things Journal, 5(5): 3758-3773, 2018, ISSN: 2327-4662.

FAWCETT, D.; PANIGADA, C.; TAGLIABUE, G.; BOSCHETTI, M.; CELESTI, M.; EVDOKIMOV, A.; BIRIUKOVA, K.; COLOMBO, R.; MIGLIETTA, F.; RASCHER, U.: “Multi-scale evaluation of drone-based multispectral surface reflectance and vegetation indices in operational conditions.”, Rem. Sens., 12(3): 514, 2020.

FENG, X.; YAN, F.; LIU, X.: “Study of wireless communication technologies on Internet of Things for precision agriculture”, Wireless Personal Communications, 108(3): 1785-1802, 2019, ISSN: 0929-6212.

FRIHA, O.; FERRAG, M.A.; SHU, L.; MAGLARAS, L.; WANG, X.: “Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies”, IEEE/CAA Journal of Automatica Sinica, 8(4): 718-752, 2021, ISSN: 2329-9266.

GAGO, J.; DOUTHE, C.; COOPMAN, R.E.; GALLEGO, P.P.; RIBAS-CARBO, M.; FLEXAS, J.; ESCALONA, J.; MEDRANO, H.: “UAVs challenge to assess water stress for sustainable agriculture”, Agricultural water management, 153: 9-19, 2015, ISSN: 0378-3774.

GAŠPAROVIĆ, M.; ZRINJSKI, M.; BARKOVIĆ, D.; RADOČAJ, D.: “An automatic method for weed mapping in oat fields based on UAV imagery”, Computers and Electronics in Agriculture, 173: 105-385, 2020, ISSN: 0168-1699.

GILL, S.S.; CHANA, I.; BUYYA, R.: “IoT based agriculture as a cloud and big data service: the beginning of digital India”, Journal of Organizational and End User Computing (JOEUC), 29(4): 1-23, 2017.

GUAN, S.; FUKAMI, K.; MATSUNAKA, H.; AL-ZAHRANI, M.; TANAKA, R.; NAKANO, H.; SAKAI, T.; NAKANO, K.; CHOI, H.-L.; TAKAHASHI, K.: “Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs”, Remote Sensing, 11(2): 112, 2019, ISSN: 2072-4292.

GUILLÉN, L.; YASELIS, P.P.; MOLINA, O.: “Drones, aplicaciones en la Agricultura de Precisión: una revisión”, Rev. Agricultura Tropical, 6(2): 1-11, 2020, ISSN: 2517-9292.

HAQUE, A.; ISLAM, N.; SAMRAT, N.H.; DEY, S.; RAY, B.: “Smart farming through responsible leadership in bangladesh: possibilities, opportunities, and beyond”, Sustainability, 13(8): 4511, 2021.

HARDIN, P.J.; HARDIN, T.J.: “Small‐scale remotely piloted vehicles in environmental research”, Geography Compass, 4(9): 1297-1311, 2010, ISSN: 1749-8198, DOI: https://doi.org/10.1111/j.1749-8198.2010.00381.x.

HARDIN, P.J.; JENSEN, R.R.: “Small-scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities”, GIScience & Remote Sensing, 48(1): 99-111, 2011, ISSN: 1548-1603, DOI: . https://doi.org/10.2747/1548-1603.48.1.99.

HE, Y.; NIE, P.; ZHANG, Q.; LIU, F.: Agricultural Internet of Things: technologies and applications, Ed. Springer, (1st ed. 2021 edition). ed., 2021.

HERNÁNDEZ, P.P.; HERNÁNDEZ, A.P.; VARGAS, R.H.; ZAMORA, H.Y.; DOPICO, V.Y.: “Determinación de normas de fertilización diferenciada para el cultivo de la papa empleando técnicas de agricultura de precisión.”, Revista Ciencias Técnicas Agropecuarias, 15(1), 2006, ISSN: 2071-0054.

HSU, sT-C.; YANG, H.; CHUNG, Y.; HSU, C.: “A creative iot agriculture platform for cloud fog computing, Sustain”, Comput. Inf. Syst, 28: 100-285, 2020.

HUNT JR, R.; DAUGHTRY, C.S.: “What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?”, International journal of remote sensing, 39(15-16): 5345-5376, 2018, ISSN: 0143-1161, 5345-5376, DOI: 10.1080/01431161.2017.1410300.

INOUE, Y.: “Satellite-and drone-based remote sensing of crops and soils for smart farming–a review”, Soil Science and Plant Nutrition, 66(6): 798-810, 2020, ISSN: 0038-0768, DOI: https://doi.org/10.1080/00380768.2020.1738899.

JINBO, C.; XIANGLIANG, C.; BANDINI, F.; LAM, A.: “Agricultural product monitoring system supported by cloud computing”, Cluster Computing, 22(4): 8929-8938, 2019, ISSN: 1386-7857.

KALISCHUK, M.; PARET, M.L.; FREEMAN, J.H.; RAJ, D.; DA SILVA, S.; EUBANKS, S.; WIGGINS, D.; LOLLAR, M.; MAROIS, J.J.; MELLINGER, C.H.: “An improved crop scouting technique incorporating unmanned aerial vehicle–assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon”, Plant disease, 103(7): 1642-1650, 2019, ISSN: 0191-2917, 1642–1650.

KHAN, P.W.; BYUN, Y.-C.; NAMJE P N: “IoT-blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning”, Sensors, 20(10): 2990, 2020, ISSN: 1424-8220.

KHANNA, A.F.; KAUR, S.: “Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture”, Computers and electronics in agriculture, 157: 218-231, 2019, ISSN: 0168-1699.

LAGKAS, T.; ARGYRIOU, V.; BIBI, S.; SARIGIANNIDIS, P.: “UAV IoT framework views and challenges: Towards protecting drones as “Things””, Sensors, 18(11): 4015, 2018, ISSN: 1424-8220, DOI: https://doi.org/10.3390/s18114015.

LAGO-GONZÁLEZ, C.; SEPÚLVEDA-PEÑA, J.C.; BARROSO-ABREU, R.; FERNÁNDEZ-PEÑA, F.O.; MACIÁ-PÉREZ, F.; LORENZO, J.: “Sistema para la generación automática de mapas de rendimiento. Aplicación en la agricultura de precisión”, Idesia (Arica), 29(1): 59-69, 2011, ISSN: 0718-3429.

LALIBERTE, A.S.; RANGO, A.: “Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands”, GIScience & Remote Sensing, 48(1): 4-23, 2011, ISSN: 1548-1603, DOI: https://doi.org/10.2747/1548-1603.48.1.4.

LALIBERTE, A.S.; RANGO, A.; HERRICK, J.: “Unmanned aerial vehicles for rangeland mapping and monitoring: A comparison of two systems”, En: ASPRS Annual Conference Proceedings, 2007.

LIAKOS, K.G.; BUSATO, P.; MOSHOU, D.; PEARSON, S.; BOCHTIS, D.: “Machine learning in agriculture: A review”, Sensors, 18(8): 2674, 2018, ISSN: 1424-8220.

LIU, S.; GUO, L.; WEBB, H.; YA, X.; CHANG, X.: “Internet of Things monitoring system of modern eco-agriculture based on cloud computing”, Ieee Access, 7: 37050-37058, 2019, ISSN: 2169-3536.

LÓPEZ-GRANADOS, F.; TORRES-SÁNCHEZ, J.; SERRANO-PÉREZ, A.; DE CASTRO, A.I.; MESAS-CARRASCOSA, F.J.; PEÑA, J.M.: “Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds”, Precision agriculture, 17: 183-199, 2016, ISSN: 1385-2256.

LORA, C.D.: “Consumo energético de la maquinaria agrícola con el empleo de técnicas de agricultura de precisión”, Revista Ingeniería Agrícola, 5(2), 2015.

MAIMAITIJIANG, M.; GHULAM, A.; SIDIKE, P.; HARTLING, S.; MAIMAITIYIMING, M.; PETERSON, K.; SHAVERS, E.; ARCIA, J.; PETERSON, J.; KADAM, S.: “Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine”, ISPRS Journal of Photogrammetry and Remote Sensing, 134: 43-58, 2017, ISSN: 0924-2716, DOI: https://doi.org/10.1016/j.isprsjprs.2017.10.011.

MANFREDA, S.; MCCABE, M.F.; MILLER, P.E.; LUCAS, R.; PAJUELO-MADRIGAL, V.; MALLINIS, G.; BEN-DOR, E.; HELMAN, D.; ESTES, L.; CIRAOLO, G.: “On the use of unmanned aerial systems for environmental monitoring”, Remote sensing, 10(4): 641, 2018, ISSN: 2072-4292.

MATAMOROS, C.P.; GARCÍA, R.E.; SOTO, M.F.; MENÉNDEZ, H.P.; MARTÍNEZ, S.F.; CRUZ, I.R.; CAPOTE, F.J.L.; OJEDA, M.D.; MENESES, D.P.; RODRIGUEZ, Q.B.; VALDIVIA, P.O.: “Agricultura de Precisión aplicada a la producción de arroz en Cuba”, En: Informática 2022. XVIII Convención y Feria Internacional. XII Congreso Internacional Geomática, La Habana 21-25 de marzo 2022, La Habana, Cuba, 2022.

MELVILLE, B.; LUCIEER, A.; ARYAL, J.: “Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian midlands”, Drones, 3(1): 5, 2019, ISSN: 2504-446X.

MOHARANA, S.; DUTTA, S.: “Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery”, ISPRS journal of photogrammetry and remote sensing, 122: 17-29, 2016, ISSN: 0924-2716.

NEBIKER, S.; ANNEN, A.; SCHERRER, M.; OESCH, D.: “A light-weight multispectral sensor for micro UAV—Opportunities for very high resolution airborne remote sensing”, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 37(B1): 1193-1200, 2008.

NEGASH, L.; KIM, H.-Y.; CHOI, H.-L.: “Emerging UAV applications in agriculture”, En: 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA), Ed. IEEE, pp. 254-257, 2019, DOI: https://doi.org/10.1109/RITAPP.2019, ISBN: 1-72813-118-9.

NIU, H.; HOLLENBECK, D.; ZHAO, T.; WANG, D.; CHEN, Y.Q.: “Evapotranspiration estimation with small UAVs in precision agriculture”, Sensors, 20(22): 6427, 2020, ISSN: 1424-8220, DOI: https://doi.org/10.3390/s20226427.

NONAMI, K.: “Prospect and recent research & development for civil use autonomous unmanned aircraft as UAV and MAV”, Journal of system Design and Dynamics, 1(2): 120-128, 2007, ISSN: 1881-3046.

PANDAY, U.; PRATIHAST, A.; ARYAL, J.: “A review on drone-based data solutions for cereal crops.”, Drones, 4(3): 1-29, 2020, DOI: https://doi.org/10.3390/ drones 403004.

PARSAEIAN, M.; SHAHABI, M.; HASSANPOUR, H.: “Estimating oil and protein content of sesame seeds using image processing and artificial neural network”, Journal of the American Oil Chemists’ Society, 97(7): 691-702, 2020, ISSN: 0003-021X.

PINCHEIRA, M.; VECCHIO, M.; GIAFFREDA, R.; KANHERE, S.S.: “Cost-effective IoT devices as trustworthy data sources for a blockchain-based water management system in precision agriculture”, Computers and Electronics in Agriculture, 180: 105889, 2021, ISSN: 0168-1699.

PURI, V.; NAYYAR, A.; RAJA, L.: “Agriculture drones: A modern breakthrough in precision agriculture”, Journal of Statistics and Management Systems, 20(4): 507-518, 2017, ISSN: 0972-0510.

RADOGLOU-GRAMMATIKIS, P.; SARIGIANNIDIS, P.; LAGKAS, T.; BOSCH, I.: “A compilation of UAV applications for precision agriculture”, Computer Networks, 172: 107148, 2020, ISSN: 1389-1286, DOI: https://doi.org/10.1016/j.comnet.2020.107148.

REJEB, A.; ABDOLLAHI, A.; REJEB, K.; TREIBLMAIER, H.: “Drones in agriculture: A review and bibliometric analysis”, Computers and electronics in agriculture, 198: 107017, 2022, ISSN: 0168-1699.

RÍOS-HERNÁNDEZ, R.: “La Agricultura de Precisión. Una necesidad actual”, Revista Ingeniería Agrícola, 11(1): 67-74, 2021, ISSN: 2306-1545, e-ISSN-2227-8761.

SHADRIN, D.; MENSHCHIKOV, A.; SOMOV, A.; BORNEMANN, G.; HAUSLAGE, J.; FEDOROV, M.: “Enabling precision agriculture through embedded sensing with artificial intelligence”, IEEE Transactions on Instrumentation and Measurement, 69(7): 4103-4113, 2019, ISSN: 0018-9456.

SOSA-ESCALONA, Y.; PEÑA CASADEVALLS, M.; SANTIESTEBAN-TOCA, C.E.: “Sistema para la alerta temprana de los efectos del cambio climático en la agricultura”, Revista Cubana de Ciencias Informáticas, 11(3): 64-76, 2017, ISSN: 2227-1899.

SOSA-FRANCO, I.; PÉREZ-GUERRA, G.; MACHADO-GARCÍA, N.; PÉREZ-RUIZ, M.E.: “Método para el procesamiento de consultas en un Sistema de Información Geográfica”, Revista Ciencias Técnicas Agropecuarias, 32(2, (April-June)), 2023, ISSN: 2071-0054.

SRIVASTAVA, K.; PANDEY, P.C.; SHARMA, J.K.: “An approach for route optimization in applications of precision agriculture using UAVs”, Drones, 4(3): 58, 2020, ISSN: 2504-446X.

SU, J.; COOMBES, M.; LIU, C.; GUO, L.; CHEN, W.-H.: “Wheat drought assessment by remote sensing imagery using unmanned aerial vehicle”, En: 2018 37th Chinese Control Conference (CCC), Ed. IEEE, pp. 10340-10344, 2018a, ISBN: 988-15639-5-X.

SU, J.; LIU, C.; AMADO, M.E.; HU, X.; WANG, C.; XU, X.; LI, Q.; GUO, L.; CHEN, W.-H.: “Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery”, Computers and electronics in agriculture, 155: 157-166, 2018b, ISSN: 0168-1699, DOI: https://doi.org/10.1016/j.compag.2018.10.017.

TANG, Y.; DANANJAYAN, S.; HOU, C.; GUO, Q.; LUO, S.; HE, Y.: “A survey on the 5G network and its impact on agriculture: Challenges and opportunities”, Computers and Electronics in Agriculture, 180: 105895, 2021, ISSN: 0168-1699.

TANTALAKI, N.; SOURAVLAS, S.; ROUMELIOTIS, M.: “Data-driven decision making in precision agriculture: The rise of big data in agricultural systems”, Journal of agricultural & food information, 20(4): 344-380, 2019, ISSN: 1049-6505.

TAO, H.; CHOI, H.-L.; XU, L.; MIAO, M.; YANG, G.; YANG, X.; FAN, L.: “Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images”, Sensors, 20(4): 1231, 2020, ISSN: 1424-8220.

TSOUROS, D.; BIBI, S.; SARIGIANNIDIS, P.: “A review on UAV-based applications for precision agriculture”, Information, 10(11): 349, 2019, ISSN: 2078-2489, DOI: https://doi.org/10.3390/info10110349.

TZOUNIS, A.F.; KATSOULAS, N.; BARTZANAS, T.; KITTAS, C.: “Internet of Things in agriculture, recent advances and future challenges”, Biosystems engineering, 164: 31-48, 2017, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2017.09.007.

VELUSAMY, P.; BARTH, S.R.; MAHENDRAN, R.; NASEER, S.; AMADO, M.E.; CHOI, J.-G.: “Unmanned Aerial Vehicles (UAV) in precision agriculture: Applications and challenges”, Energies, 15(1): 217, 2021, ISSN: 1996-1073, DOI: https://doi.org/10.3390/en15010217.

ZAMORA-IZQUIERDO, M.A.; SANTA, J.; MARTÍNEZ, J.A.; MARTÍNEZ, V.; SKARMETA, A.F.: “Smart farming IoT platform based on edge and cloud computing”, Biosystems engineering, 177: 4-17, 2019, ISSN: 1537-5110.

ZHANG, C.; KOVACS, J.M.: “The application of small unmanned aerial systems for precision agriculture: a review”, Precision agriculture, 13: 693-712, 2012, ISSN: 1385-2256, DOI: https://doi.org/10.1007/s11119-012-9274-5.

ZHANG, L.; ZHANG, H.; NIU, Y.; HAN, W.: “Mapping maize water stress based on UAV multispectral remote sensing”, Remote Sensing, 11(6): 605, 2019, ISSN: 2072-4292.

ZHENG, J.; YANG, W.: “Design of a Precision Agriculture Leakage Seeding System Based on Wireless Sensors.”, International Journal of Online Engineering, 14(5), 2018, ISSN: 1868-1646.

ZHOU, Y.; XIE, Y.; SHAO, L.: “Simulation of the core technology of a greenhouse monitoring system based on a wireless sensor network”, Int. J. Online Eng, 12(05): 43, 2016.

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