Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical

Autores:: López, Jhony F.
Pérez, William

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Universidad de Cundinamarca

Repositorio:: Repositorio UdeC

Idioma:

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dc.title.spa.fl_str_mv	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
title	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
spellingShingle	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical AaaS cashew economic feasibility large-scale multisite reproducibility NDRE NDV operational validation precision agriculture variable rate application agricultura de precisión anacardo tropical aplicación de tasa variable drones multiespectrales NDRE NDVI reproducibilidad multisitio validación operacional viabilidad económica escalabilidad
title_short	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
title_full	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
title_fullStr	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
title_full_unstemmed	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
title_sort	Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
dc.creator.fl_str_mv	López, Jhony F. Pérez, William
dc.contributor.author.spa.fl_str_mv	López, Jhony F. Pérez, William
dc.subject.none.fl_str_mv	AaaS cashew economic feasibility large-scale multisite reproducibility NDRE NDV operational validation precision agriculture variable rate application agricultura de precisión anacardo tropical aplicación de tasa variable drones multiespectrales NDRE NDVI reproducibilidad multisitio validación operacional viabilidad económica escalabilidad
topic	AaaS cashew economic feasibility large-scale multisite reproducibility NDRE NDV operational validation precision agriculture variable rate application agricultura de precisión anacardo tropical aplicación de tasa variable drones multiespectrales NDRE NDVI reproducibilidad multisitio validación operacional viabilidad económica escalabilidad
publishDate	2025
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dc.date.available.none.fl_str_mv	2025-06-18 21:26:21
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.citationedition.spa.fl_str_mv	Núm. 26 , Año 2025 : Negonotas Docentes
dc.relation.citationissue.spa.fl_str_mv	26
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dc.relation.references.none.fl_str_mv	Acharya, B., O’Quinn, T. N., Everman, W. J. y Mehl, H. L. (2019). Effectiveness of fungicides and their application timing for the management of sorghum foliar anthracnose in the Mid-Atlantic United States. Plant Disease, 103(11), 2804–2811. https://doi.org/10.1094/PDIS-10-18-1867-RE Ahn, M. I. y Yun, S. C. (2009). Epidemiological investigations to optimize the management of pepper anthracnose. Plant Pathology Journal, 25(3), 213–219. https://doi.org/10.5423/PPJ.2009.25.3.213 Alharasees, O., Adali, O. H. y Kale, U. (2023). Human factors in the age of autonomous UAVs: Impact of artificial intelligence on operator performance and safety. En 2023 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 344–351). https://doi.org/10.1109/ICUAS57906.2023.10156037 Alemán-Montes, B., Henríquez-Henríquez, C., Largaespada-Zapata, K. y Ramírez-Rodríguez, T. (2022). Evaluación de flecha seca en palma aceitera (Elaeis guineensis Jacq.) mediante imágenes multiespectrales obtenidas con VANT. Agronomía Mesoamericana, 33(2), 47557. https://doi.org/10.15517/am.v33i2.47557 Alvarez-Vanhard, E., Corpetti, T. y Houet, T. (2021). UAV y satellite synergies for optical remote sensing applications: A literature review. Science of Remote Sensing, 4, 100019. https://doi.org/10.1016/j.srs.2021.100019 Arcadia, É. A., Marceleño, S. M. L. y Flores, F. (2024). Agricultura de precisión en la producción de caña de azúcar: Diagnóstico para revisar las relaciones entre prácticas agrícolas tradicionales y la adopción de tecnologías. En E. I. Mariscal, M. E. Becerra, R. Gómez y L. C. Barrón (coords.), Desafíos en el contexto empresarial: sostenibilidad, innovación y competitividad (pp. 35–47). Universidad Autónoma de Nayarit. https://doi.org/10.52501/cc.250.02 Banik, T. y Vn, N. (2024). Farming in the digital age: Unleashing the power of farming as a service (FaaS). International Journal of Agriculture Extension and Social Development, 7(5), 99–102. https://doi.org/10.33545/26180723.2024. v7.i5b.601 Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), 40. https://doi.org/10.3390/DRONES3020040 Barocco, R. L., Clohessy, J. W., O’Brien, G. K., Dufault, N. S., Anco, D. J. y Small, I. M. (2024). Sensor-based quantification of peanut disease defoliation using an unmanned aircraft system and multispectral imagery. Plant Disease, 108(2), 416–425. https://doi.org/10.1094/PDIS-05-23-0847-RE Canicattì, M. y Vallone, M. (2024). Drones in vegetable crops: A systematic literature review. Smart Agricultural Technology, 7, 100396. https://doi.org/10.1016/j.atech.2024.100396 Conner, R. L., McAndrew, D. W., Kiehn, F. A., Chapman, S. R. y Froese, N. T. (2004). Effect of foliar fungicide application timing on the control of bean anthracnose in the navy bean ‘Navigator’. Canadian Journal of Plant Pathology, 26(3), 299–303. https://doi.org/10.1080/07060660409507147 Costa, R., Almeida, C. y Laurindo, F. (2022). Precision farming-as-a-service: Fundamental concepts, trends and challenges. A new business model into agricultural segment. En 19th International Conference on Information Systems and Technology Management. https://doi.org/10.5748/19contecsi/pse/agb/7067 DaMatta, F. M., Avila, R. T., Cardoso, A. A., Martins, S. C. V. y Ramalho, J. C. (2018). Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: A review. Journal of Agricultural and Food Chemistry, 66(21), 5264-5274. https://doi.org/10.1021/acs.jafc.7b0453 Kwao, P. L., Owusu, G. M., Okyere, J., Agbenya, J. K., Laryea, I. L. N. y Armah, S. K. (2024). Agricultural drones in Africa: Exploring adoption, applications, and barriers. International Journal for Multidisciplinary Research, 6(6). https://doi.org/10.36948/ijfmr.2024.v06i06.28326 Mena, E., Galeana, G., Estrada, M., Alonso, E. P. y Flores, D. A. (2025). Tecnologías innovadoras en la agricultura de precisión. Ciencia Latina Revista Científica Multidisciplinar, 9(2), 5660–5666. https://doi.org/10.37811/cl_rcm. v9i2.17319 Mhaned, A., Salma, M., Haji, M. E. y Benhra, J. (2025). Smart agriculture based on artificial intelligence and drones: A systematic review. En M. Syafrudin, N. Fitriyani y M. Anshari (eds.), Artificial Intelligence and Data Science for Sustainability: Applications and Methods (pp. 213-266). IGI Global Scientific Publishing. https://doi. org/10.4018/979-8-3693-6829-9.ch008 MicaSense Inc. (2025). MicaSense RedEdge-P camera technical specifications. https://support.micasense.com/hc/en-us/ articles/4410824602903-RedEdge-P-Integration-Guide MicaSense Support Team. (2022). Best practices: Collecting data with MicaSense sensors. https://support.micasense.com/ hc/en-us/articles/224893167 Monteiro, F., Romeiras, M., Bernabé, J., Catarino, S., Batista, D. y Sebastiana, M. (2022). Disease-causing agents in cashew: A review in a tropical cash crop context. Agronomy, 12(10), 2553. https://doi.org/10.3390/agronomy12102553 Nagel, J. (2012). Principales barreras para la adopción de las TIC en la agricultura y en las áreas rurales. Cepal. https://www. cepal.org/es/publicaciones/4011-principales-barreras-la-adopcion-tic-la-agricultura-areas-rurales Nicolau, A., Tăbîrcă, A. I., Tănase, L. C. y Radu, V. (2025). Integrating drone-based decision support systems in precision farming: An econometric simulation of management efficiency and cost-benefit analysis. Romanian Agricultural Research. https://doi.org/10.59665/rar4285 Njoroge, S., Mugi-Ngenga, E., Limo, B. y Fakoya, O. E. (2025). Precision agriculture in Africa: Challenges and opportunities. Growing Africa, 4(1), 2–5. https://doi.org/10.55693/ga41.mbuf4046 Pham, Y., Reardon-Smith, K., Mushtaq, S. y Cockfield, G. (2019). The impact of climate change and variability on coffee production: A systematic review. Climatic Change, 16, 609-630. https://doi.org/10.1007/s10584-019-02538-y Prasad, K., Venkatesa, P. N. B., Rohini, A., Kalpana, M., Parameswari, E. y Kowsalya, S. (2025). Exploring the impact of drone technology on agricultural practices: A bibliometric review. Plant Science Today, 12(sp1). https://doi. org/10.14719/pst.10165 Rishikesavan, S., Kannan, P., Pazhanivelan, S., Kumaraperumal, R., Sritharan, N., Muthumanickam, D., Firnass, M. M. R. A., Baskaran, V. y Teja, V. S. (2024). Prospects and challenges of drone technology in sustainable agriculture. Plant Science Today, 11(sp4). https://doi.org/10.14719/pst.5761 Rodríguez-López, E. S., Cárdenas-Soriano, E., Hernández-Delgado, S., Gutiérrez-Díez, A. y Mayek-Pérez, N. (2013). Análisis de la infección de Colletotrichum gloeosporioides (Penz.) Penz. y Sacc. de frutos de aguacatero. Revista Brasileira de Fruticultura, 35(3), 747–756. https://doi.org/10.1590/S0100-29452013000300029 Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K. T., Peterson, J., Burken, J. y Fritschi, F. (2019). UAV/satellite multiscale data fusion for crop monitoring and early stress detection. En ISPRS Geospatial Week 2019 (Vol. XLII-2/W13, pp. 715–722). International Society of Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-2-W13-715-2019 Salas-Macías, C. A., Sánchez-Mora, F., Montes Escobar, K., de la Hoz-M, J., Limongi-Andrade, R., Mora-Yela, R. V. y Garcés-Fiallos, F. R. (2024). Resilience of cacao-based agroforestry systems to climate change. En L. García, N. Maddela, F. Zambrano y C. Aguilar (eds.), Sustainable Cacao Cultivation in Latin America (pp. 115–134). Routledge. https://doi.org/10.4324/9781003381761-8 Satish, S., Shirwal, S., Abishek, A. G., Maheshwari y Murali, M. (2025). Application of drones in precision agriculture: A review on benefits and challenges. Journal of Experimental Agriculture International, 47(7), 516–531. https:// doi.org/10.9734/jeai/2025/v47i73591 Singh, E., Pratap, A. y Kumar, A. (2024). Smart agriculture drone for crop spraying using image-processing and machine learning techniques: experimental validation. IoT, 5(2), 348–367. https://doi.org/10.3390/iot5020013 Sreeram, M. y Nof, S. Y. (2021). Human-in-the-loop: Role in cyber-physical agricultural systems. International Journal of Computers Communications & Control, 16(2). https://doi.org/10.15837/IJCCC.2021.2.4166 Telefónica Tech. (2025, septiembre). Drones, AI and IoT in precision agriculture: Innovation across the phenological cycle. https://telefonicatech.com/en/blog/drones-ai-and-iot-in-precision-agriculture-innovation-across-thephenological-cycle Vanitha, N. y Selvaa, S. K. R. (2023). Analysis of drone applications in precision agriculture. En G. Karthick (ed.), Contemporary Developments in Agricultural Cyber-Physical Systems (pp. 240-253). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-7879-0.ch013 Wingtra AG. (2025). WingtraOne GEN II technical specifications. https://wingtra.com/mapping-drone-wingtraone/ technical-specifications/ Xu, K., Gong, Y., Fang, S., Wang, K., Lin, Z. y Wang, F. (2021). Radiometric calibration of UAV-based multispectral imagery: A critical review for quantitative analysis. Remote Sensing, 11(11), 1291. https://doi.org/10.3390/ rs11111291 Yılmaz, A. A. (2024). Enhancing UAV crew performance and safety: A technology and innovation management perspective. Sosyal Mucit Academic Review, 5(2), 130–153. https://doi.org/10.54733/smar.1512893 Zhao, J. (2024). Drone technology for precision agriculture: Advancements and optimization strategies. Highlights in Science Engineering and Technology, 111, 185–191. https://doi.org/10.54097/h70j2c34 Zhou, Q., Zhang, S., Xue, X., Cai, C. y Wang, B. (2023). Performance evaluation of UAVs in wheat disease control. Agronomy, 13(8), 2131. https://doi.org/10.3390/agronomy13082131
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spelling	López, Jhony F.Pérez, William2025-06-18 21:26:212025-11-05T15:35:05Z2025-06-18 21:26:21%21-%06-%182346-1357https://repositorio.cun.edu.co/handle/cun/1106010.52143/2346-1357.11202711-3329https://doi.org/10.52143/2346-1357.1120application/pdfFondo Editorial CUNNegonotas Docentes - 2025https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.cun.edu.co/index.php/negonotas/article/view/1120AaaScasheweconomic feasibilitylarge-scalemultisite reproducibilityNDRENDVoperational validationprecision agriculturevariable rate applicationagricultura de precisiónanacardo tropicalaplicación de tasa variabledrones multiespectralesNDRENDVIreproducibilidad multisitiovalidación operacionalviabilidad económicaescalabilidadValidación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropicalArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionhttps://revistas.cun.edu.co/index.php/negonotas/article/download/1120/801Núm. 26 , Año 2025 : Negonotas Docentes26Negonotas DocentesAcharya, B., O’Quinn, T. N., Everman, W. J. y Mehl, H. L. (2019). Effectiveness of fungicides and their application timing for the management of sorghum foliar anthracnose in the Mid-Atlantic United States. Plant Disease, 103(11), 2804–2811. https://doi.org/10.1094/PDIS-10-18-1867-RE Ahn, M. I. y Yun, S. C. (2009). Epidemiological investigations to optimize the management of pepper anthracnose. Plant Pathology Journal, 25(3), 213–219. https://doi.org/10.5423/PPJ.2009.25.3.213 Alharasees, O., Adali, O. H. y Kale, U. (2023). Human factors in the age of autonomous UAVs: Impact of artificial intelligence on operator performance and safety. En 2023 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 344–351). https://doi.org/10.1109/ICUAS57906.2023.10156037 Alemán-Montes, B., Henríquez-Henríquez, C., Largaespada-Zapata, K. y Ramírez-Rodríguez, T. (2022). Evaluación de flecha seca en palma aceitera (Elaeis guineensis Jacq.) mediante imágenes multiespectrales obtenidas con VANT. Agronomía Mesoamericana, 33(2), 47557. https://doi.org/10.15517/am.v33i2.47557 Alvarez-Vanhard, E., Corpetti, T. y Houet, T. (2021). UAV y satellite synergies for optical remote sensing applications: A literature review. Science of Remote Sensing, 4, 100019. https://doi.org/10.1016/j.srs.2021.100019 Arcadia, É. A., Marceleño, S. M. L. y Flores, F. (2024). Agricultura de precisión en la producción de caña de azúcar: Diagnóstico para revisar las relaciones entre prácticas agrícolas tradicionales y la adopción de tecnologías. En E. I. Mariscal, M. E. Becerra, R. Gómez y L. C. Barrón (coords.), Desafíos en el contexto empresarial: sostenibilidad, innovación y competitividad (pp. 35–47). Universidad Autónoma de Nayarit. https://doi.org/10.52501/cc.250.02 Banik, T. y Vn, N. (2024). Farming in the digital age: Unleashing the power of farming as a service (FaaS). International Journal of Agriculture Extension and Social Development, 7(5), 99–102. https://doi.org/10.33545/26180723.2024. v7.i5b.601 Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), 40. https://doi.org/10.3390/DRONES3020040 Barocco, R. L., Clohessy, J. W., O’Brien, G. K., Dufault, N. S., Anco, D. J. y Small, I. M. (2024). Sensor-based quantification of peanut disease defoliation using an unmanned aircraft system and multispectral imagery. Plant Disease, 108(2), 416–425. https://doi.org/10.1094/PDIS-05-23-0847-RE Canicattì, M. y Vallone, M. (2024). Drones in vegetable crops: A systematic literature review. Smart Agricultural Technology, 7, 100396. https://doi.org/10.1016/j.atech.2024.100396 Conner, R. L., McAndrew, D. W., Kiehn, F. A., Chapman, S. R. y Froese, N. T. (2004). Effect of foliar fungicide application timing on the control of bean anthracnose in the navy bean ‘Navigator’. Canadian Journal of Plant Pathology, 26(3), 299–303. https://doi.org/10.1080/07060660409507147 Costa, R., Almeida, C. y Laurindo, F. (2022). Precision farming-as-a-service: Fundamental concepts, trends and challenges. A new business model into agricultural segment. En 19th International Conference on Information Systems and Technology Management. https://doi.org/10.5748/19contecsi/pse/agb/7067 DaMatta, F. M., Avila, R. T., Cardoso, A. A., Martins, S. C. V. y Ramalho, J. C. (2018). Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: A review. Journal of Agricultural and Food Chemistry, 66(21), 5264-5274. https://doi.org/10.1021/acs.jafc.7b0453 Kwao, P. L., Owusu, G. M., Okyere, J., Agbenya, J. K., Laryea, I. L. N. y Armah, S. K. (2024). Agricultural drones in Africa: Exploring adoption, applications, and barriers. International Journal for Multidisciplinary Research, 6(6). https://doi.org/10.36948/ijfmr.2024.v06i06.28326 Mena, E., Galeana, G., Estrada, M., Alonso, E. P. y Flores, D. A. (2025). Tecnologías innovadoras en la agricultura de precisión. Ciencia Latina Revista Científica Multidisciplinar, 9(2), 5660–5666. https://doi.org/10.37811/cl_rcm. v9i2.17319 Mhaned, A., Salma, M., Haji, M. E. y Benhra, J. (2025). Smart agriculture based on artificial intelligence and drones: A systematic review. En M. Syafrudin, N. Fitriyani y M. Anshari (eds.), Artificial Intelligence and Data Science for Sustainability: Applications and Methods (pp. 213-266). IGI Global Scientific Publishing. https://doi. org/10.4018/979-8-3693-6829-9.ch008 MicaSense Inc. (2025). MicaSense RedEdge-P camera technical specifications. https://support.micasense.com/hc/en-us/ articles/4410824602903-RedEdge-P-Integration-Guide MicaSense Support Team. (2022). Best practices: Collecting data with MicaSense sensors. https://support.micasense.com/ hc/en-us/articles/224893167 Monteiro, F., Romeiras, M., Bernabé, J., Catarino, S., Batista, D. y Sebastiana, M. (2022). Disease-causing agents in cashew: A review in a tropical cash crop context. Agronomy, 12(10), 2553. https://doi.org/10.3390/agronomy12102553 Nagel, J. (2012). Principales barreras para la adopción de las TIC en la agricultura y en las áreas rurales. Cepal. https://www. cepal.org/es/publicaciones/4011-principales-barreras-la-adopcion-tic-la-agricultura-areas-rurales Nicolau, A., Tăbîrcă, A. I., Tănase, L. C. y Radu, V. (2025). Integrating drone-based decision support systems in precision farming: An econometric simulation of management efficiency and cost-benefit analysis. Romanian Agricultural Research. https://doi.org/10.59665/rar4285 Njoroge, S., Mugi-Ngenga, E., Limo, B. y Fakoya, O. E. (2025). Precision agriculture in Africa: Challenges and opportunities. Growing Africa, 4(1), 2–5. https://doi.org/10.55693/ga41.mbuf4046 Pham, Y., Reardon-Smith, K., Mushtaq, S. y Cockfield, G. (2019). The impact of climate change and variability on coffee production: A systematic review. Climatic Change, 16, 609-630. https://doi.org/10.1007/s10584-019-02538-y Prasad, K., Venkatesa, P. N. B., Rohini, A., Kalpana, M., Parameswari, E. y Kowsalya, S. (2025). Exploring the impact of drone technology on agricultural practices: A bibliometric review. Plant Science Today, 12(sp1). https://doi. org/10.14719/pst.10165 Rishikesavan, S., Kannan, P., Pazhanivelan, S., Kumaraperumal, R., Sritharan, N., Muthumanickam, D., Firnass, M. M. R. A., Baskaran, V. y Teja, V. S. (2024). Prospects and challenges of drone technology in sustainable agriculture. Plant Science Today, 11(sp4). https://doi.org/10.14719/pst.5761 Rodríguez-López, E. S., Cárdenas-Soriano, E., Hernández-Delgado, S., Gutiérrez-Díez, A. y Mayek-Pérez, N. (2013). 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Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical

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