Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca

El modelado de la propagación de ondas en un canal de telecomunicaciones es una de las tareas más importantes para los ingenieros encargados de la planificación de redes inalámbricas, dentro de las cuales se encuentrasn las redes inalámbricas de sensores. De la eficiencia de los modelos usados depen...

Full description

Autores:: Barrios-Ulloa, Alexis

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2024

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: spa

id	RCUC2_dd9aa4905babbc21ee047de27eadbce7
oai_identifier_str	oai:repositorio.cuc.edu.co:11323/13114
network_acronym_str	RCUC2
network_name_str	REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca
title	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca
spellingShingle	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca Cultivos de yuca Modelos de pérdida de ruta Propagación de ondas de radio Redes inalámbricas de sensores Machine learning Cassava bushes Path los models Modelling wave propagation Wireless sensor networks
title_short	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca
title_full	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca
title_fullStr	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca
title_full_unstemmed	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca
title_sort	Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca
dc.creator.fl_str_mv	Barrios-Ulloa, Alexis
dc.contributor.advisor.none.fl_str_mv	Cama-Pinto, Alejandro De-La-Hoz-Franco, Emiro
dc.contributor.author.none.fl_str_mv	Barrios-Ulloa, Alexis
dc.contributor.jury.none.fl_str_mv	Salcedo Morillo, Dixon Silva Cárdenas, Carlos Sierra Carrillo, Javier
dc.subject.proposal.spa.fl_str_mv	Cultivos de yuca Modelos de pérdida de ruta Propagación de ondas de radio Redes inalámbricas de sensores
topic	Cultivos de yuca Modelos de pérdida de ruta Propagación de ondas de radio Redes inalámbricas de sensores Machine learning Cassava bushes Path los models Modelling wave propagation Wireless sensor networks
dc.subject.proposal.eng.fl_str_mv	Machine learning Cassava bushes Path los models Modelling wave propagation Wireless sensor networks
description	El modelado de la propagación de ondas en un canal de telecomunicaciones es una de las tareas más importantes para los ingenieros encargados de la planificación de redes inalámbricas, dentro de las cuales se encuentrasn las redes inalámbricas de sensores. De la eficiencia de los modelos usados depende en gran medida que los receptores puedan hacer una estimación adecuada del mensaje enviado desde el transmisor. Tradicionalmente, los modelos de pérdida de ruta desarrollados han sido principalmente empíricos o deterministas, con una menor porción de estocásticos. No obstante, debido a la búsqueda continua de los investigadores de métodos que mejoren la predicción de la pérdida de ruta, en la última década ha ganado fuerza el uso de Machine Learning como herramienta para reducir los niveles de error en la estimación de la atenuación. A pesar de ello, las investigaciones se concentran casi en su totalidad en el modelado para redes de telefonía móvil en entornos urbanos y suburbanos, en dejando de lado el estudio en otros entornos que pueden ser igualmente complicados y desafiantes, tal es el caso de aquellos con presencia de vegetación considerable, tal es el caso de las plantaciones agrícolas, donde el uso de sistemas de monitoreo basados en redes inalámbricas de sensores está cobrando cada vez más fuerza. Esta tesis doctoral presenta los resultados del modelamiento de las pérdidas de ruta causada por los arbustos en cultivos de yuca, considerados uno de los cultivos de mayor importancia para la sostenibilidad alimenticia en diferentes países. A pesar de su potencial, hay pocos reportes de investigaciones relacionadas con implementación de tecnologías de la información y la comunicación en este tipo de sembrados. La disertación se basa en la evaluación del rendimiento de algoritmos de Machine Learning que han sido entrenados utilizando mediciones reales. Como resultado, los modelos obtenidos mejoran significativamente la predicción de la pérdida de ruta en comparación con los resultados de los modelos de vegetación canónicos, alcanzando valores de error medio cuadrático (RMSE), error medio aritmético (MAE) por debajo de los 4 dB y coeficiente de determinación por encima del 98%.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-04T15:53:35Z
dc.date.available.none.fl_str_mv	2024-07-04T15:53:35Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/11323/13114
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.cuc.edu.co/
url	https://hdl.handle.net/11323/13114 https://repositorio.cuc.edu.co/
identifier_str_mv	Corporación Universidad de la Costa REDICUC - Repositorio CUC
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	[1] ONU, “World Population Prospects 2022,” 2022. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of _results.pdf (accessed Mar. 23, 2024). [2] FAO, “Seguimiento del progreso en los indicadores de los ODS relacionados con la alimentación y la agricultura 2022,” Roma, 2022. [3] UNDP, “The SDGS In action,” 2022. https://www.undp.org/sustainable-developmentgoals?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_ca mpaign=CENTRAL&c_src=CENTRAL&c_src2=GSR&gclid=EAIaIQobChMIv8W0rCC9wIVjhmtBh1Q5gOmEAAYASAAEgKvC_D_BwE (accessed Apr. 07, 2022). [4] FAO, “La agricultura mundial en la perspectiva del año 2050,” Roma, 2009. [Online]. Available: http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/Issues_papers_SP/La_agricultura_mundial.p df. [5] A. Janket et al., “Quantitative evaluation of macro-nutrient uptake by cassava in a tropical savanna climate,” Agric., vol. 11, no. 12, pp. 1–22, 2021, doi: 10.3390/agriculture11121199. [6] R. O. Enesi, S. Hauser, P. Pypers, C. Kreye, M. Tariku, and J. Six, “Understanding changes in cassava root dry matter yield by different planting dates, crop ages at harvest, fertilizer application and varieties,” Eur. J. Agron., vol. 133, p. 126448, 2022, doi: 10.1016/j.eja.2021.126448. [7] G. S. G. Byju, “Mineral nutrition of cassava,” in Advances in Agronomy, 2020, pp. 169–235. [8] FAO, “Cassava’s huge potential as 21st Century crop,” 2013. https://www.fao.org/news/story/en/item/176780/icode/ (accessed Apr. 08, 2022). [9] World Economic Forum, “This root vegetable could help alleviate hunger and end soil erosion. Here’s how,” 2021. https://www.weforum.org/agenda/2021/02/cassava-root-end-soil-erosion-deforestation/ (accessed Aug. 28, 2023). [10] E. A. Rosero Alpala, H. Ceballos Lascano, and E. Rodríguez Henao, “Aportes y perspectivas del mejoramiento genético de yuca para el fortalecimiento de su red de valor en Colombia,” AGROSAVIA, 2023. https://editorial.agrosavia.co/index.php/publicaciones/catalog/view/315/308/1875-1 (accessed Oct. 14, 2023). [11] AGRONET, “Reporte: Área, Producción y Rendimiento Nacional por Cultivo,” Ministerio de Agricultura y Desarrollo Rural, 2024. https://www.agronet.gov.co/estadistica/Paginas/home.aspx?cod=3 (accessed Mar. 23, 2024). [12] Ministerio de Agricultura y Desarrollo Rural, “Cadena productiva de la yuca,” 2021. https://sioc.minagricultura.gov.co/Yuca/Documentos/2021-03-31 Cifras Sectoriales yuca.pdf (accessed Oct. 14, 2023). [13] Economic Research Service, “Population and income drive world food production projections,” 2023. https://www.ers.usda.gov/data-products/chart-gallery/gallery/chart-detail/?chartId=108060 (accessed Jun. 08, 2024). [14] M. Dutta and K. Anand, “Role of Information Communication Technology in Agriculture,” Int. J. Nov. Res. Dev., vol. 8, no. 10, pp. 863–870, 2023. [15] P. Rajkhowa and H. Baumüller, “Assessing the potential of ICT to increase land and labour productivity in agriculture: Global and regional perspectives,” J. Agric. Econ., pp. 477–503, 2024, doi: 10.1111/1477- 9552.12566. [16] GAO, “Precision Agriculture: Benefits and Challenges for Technology Adoption and Use,” 2024. https://www.gao.gov/products/gao-24-105962 (accessed Jun. 07, 2024). [17] M. San Emeterio de la Parte, J. Martínez-Ortega, V. Hernández, and N. Martínez, “Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability,” J. Big Data, vol. 10, no. 52, 2023, doi: 10.1186/s40537-023-00729-0. [18] P. Musa, H. Sugeru, and E. P. Wibowo, “Wireless Sensor Networks for Precision Agriculture: A Review of NPK Sensor Implementations,” Sensors, vol. 24, no. 1, p. 51, 2024, doi: https://doi.org/10.3390/s24010051. [19] L. Sevgi, “Groundwave modeling and simulation strategies and path loss prediction virtual tools,” IEEE Trans. Antennas Propag., vol. 55, no. 6 I, pp. 1591–1598, 2007, doi: 10.1109/TAP.2007.897256. [20] J. Stewart, R. Stewart, and S. Kennedy, “Internet of Things - Propagation Modelling for Precision Agriculture Applications,” in Proceedings of the IEEE International Conference Image Information Processing, 2019, pp. 1–8, doi: 10.1109/ICIIP47207.2019.8985688. [21] P. Moreno-Cadena et al., “Modeling growth, development and yield of cassava: A review,” F. Crop. Res., vol. 267, no. March, p. 108140, 2021, doi: 10.1016/j.fcr.2021.108140. [22] The Nippon Foundation and CIAT, The Cassava Handbook. 2012. [23] C. C. Chen, J. Y. Ba, T. J. Li, C. C. K. Chan, K. C. Wang, and Z. Liu, “EfficientNet: A low-bandwidth IoT image sensor framework for cassava leaf disease classification,” Sensors Mater., vol. 33, no. 11, pp. 4031– 4044, 2021, doi: 10.18494/SAM.2021.3526. [24] FAO, “Cultivos y productos de ganadería,” 2021. https://www.fao.org/faostat/es/#data/QCL/visualize (accessed Aug. 28, 2023). [25] J. Aristizábal and T. Sánchez, “Guía técnica para producción y análisis de almidón de yuca,” Fao, 2007. http://www.fao.org/3/a-a1028s.pdf (accessed Apr. 12, 2022). [26] J. G. Caicedo-Ortiz et al., “Monitoring system for agronomic variables based in WSN technology on cassava crops,” Comput. Electron. Agric., vol. 145, no. August 2017, pp. 275–281, 2018, doi: 10.1016/j.compag.2018.01.004. [27] A. Bula, “Importancia de la Agricultura en el Desarollo Socio-Económico,” Universidad Nacional del Rosario, 2020. Accessed: Apr. 15, 2022. [Online]. Available: https://observatorio.unr.edu.ar/wpcontent/uploads/2020/08/Importancia-de-la-agricultura-en-el-desarrollo-socio-económico.pdf. [28] The World Bank, “Agriculture and Food,” 2023. https://www.worldbank.org/en/topic/agriculture/overview#:~:text=Agriculture can help reduce poverty,and work mainly in farming. (accessed Aug. 28, 2023). [29] D. P. Roberts, N. M. Short, J. Sill, D. K. Lakshman, X. Hu, and M. Buser, “Precision agriculture and geospatial techniques for sustainable disease control,” Indian Phytopathol., vol. 74, no. 2, pp. 287–305, 2021, doi: 10.1007/s42360-021-00334-2. [30] D. K. Rathinam, D. Surendran, A. Shilpa, A. Santhiya Grace, and J. Sherin, “Modern Agriculture Using Wireless Sensor Network (WSN),” in 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 2019, pp. 515–519, doi: 10.1109/ICACCS.2019.8728284. [31] M. E. Mondejar et al., “Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet,” Sci. Total Environ., vol. 794, no. June, 2021, doi: 10.1016/j.scitotenv.2021.148539. [32] V. Saiz-Rubio and F. Rovira-Más, “From smart farming towards agriculture 5.0: A review on crop data management,” Agronomy, vol. 10, no. 2, 2020, doi: 10.3390/agronomy10020207. [33] ISPA, “Precision Ag Definition,” 2018. https://www.ispag.org/about/definition (accessed Apr. 15, 2022). [34] M. A. Dayioğlu and U. Türker, “Digital transformation for sustainable future-agriculture 4.0: A review,” J. Agric. Sci., vol. 27, no. 4, pp. 373–399, 2021, doi: 10.15832/ankutbd.986431. [35] D. C. Rose and J. Chilvers, “Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming,” Front. Sustain. Food Syst., vol. 2, no. December, pp. 1–7, 2018, doi: 10.3389/fsufs.2018.00087. [36] S. O. Araújo, R. S. Peres, J. Barata, F. Lidon, and J. C. Ramalho, “Characterising the agriculture 4.0 landscape— emerging trends, challenges and opportunities,” Agronomy, vol. 11, no. 4, pp. 1–37, 2021, doi: 10.3390/agronomy11040667. [37] A. Monteiro, S. Santos, and P. Gonçalves, “Precision agriculture for crop and livestock farming—Brief review,” Animals, vol. 11, no. 8, pp. 1–18, 2021, doi: 10.3390/ani11082345. [38] V. J. P. D. Martinho and R. de P. F. Guiné, “Integrated-smart agriculture: Contexts and assumptions for a broader concept,” Agronomy, vol. 11, no. 8, p. 1568, 2021, doi: 10.3390/agronomy11081568. [39] I. Cisternas, I. Velásquez, A. Caro, and A. Rodríguez, “Systematic literature review of implementations of precision agriculture,” Comput. Electron. Agric., vol. 176, p. 105626, 2020, doi: 10.1016/j.compag.2020.105626. [40] S. A. Kumar and P. Ilango, “The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review,” Wirel. Pers. Commun., vol. 98, pp. 685–698, 2018, doi: https://doi.org/10.1007/s11277-017-4890-z. [41] W. Dargie and C. Poellabauer, Fundamentals of Wireless Sensor Networks, Primera. Wiley, 2010. [42] T. Ojha, S. Misra, and N. S. Raghuwanshi, “Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges,” Comput. Electron. Agric., vol. 118, pp. 66–84, 2015, doi: 10.1016/j.compag.2015.08.011. [43] K. Sohraby, D. Minoli, and T. Znati, Wireless Sensor Networks. 2007. [44] D. Thakur, Y. Kumar, A. Kumar, and P. K. Singh, Applicability of Wireless Sensor Networks in Precision Agriculture: A Review. Springer US, 2019. [45] R. P. Sharma, D. Ramesh, P. Pal, S. Tripathi, and C. Kumar, “Crop Pest Prediction,” IEEE Internet Things J., vol. 9, no. 4, pp. 3037–3045, 2022, doi: 10.1109/JIOT.2021.3094198. [46] A. K. M. Al-Qurabat, “A Lightweight Huffman-based Differential Encoding Lossless Compression Technique in IoT for Smart Agriculture,” Int. J. Comput. Digit. Syst., vol. 11, no. 1, pp. 117–127, 2022, doi: 10.12785/ijcds/110109. [47] S. Khairunnniza-Bejo, N. H. Ramli, and F. M. Muharam, “Wireless sensor network (WSN) applications in plantation canopy areas: A review,” Asian J. Sci. Res., vol. 11, no. 2, pp. 151–161, 2018, doi: 10.3923/ajsr.2018.151.161. [48] M. Srbinovska, C. Gavrovski, V. Dimcev, A. Krkoleva, and V. Borozan, “Environmental parameters monitoring in precision agriculture using wireless sensor networks,” J. Clean. Prod., vol. 88, pp. 297–307, 2015, doi: 10.1016/j.jclepro.2014.04.036. [49] N. Sabri, S. S. Mohammed, S. Fouad, A. A. Syed, F. T. Al-Dhief, and A. Raheemah, “Investigation of Empirical Wave Propagation Models in Precision Agriculture,” in MATEC Web of Conferences, 2018, vol. 150, p. 06020, doi: https://doi.org/10.1051/matecconf/201815006020. [50] N. Sabri, S. A. Aljunid, M. S. Salim, and S. Fouad, “Wireless Sensor Network Wave Propagation in Vegetation,” Recent Trends Phys. Mater. Sci. Technol. Springer Ser. Mater. Sci., vol. 204, pp. 283–298, 2015, doi: https://doi.org/10.1007/978-981-287-128-2_18. [51] ITU-R, “ITU-R Recommendation P.833-7 Attenuation in vegetation,” 2012. [52] D. L. Ndzi et al., “Vegetation attenuation measurements and modeling in plantations for wireless sensor network planning,” Prog. Electromagn. Res. B, vol. 36, pp. 283–301, 2011, doi: 10.2528/PIERB11091908. [53] T. O. Olasupo, A. Alsayyari, C. E. Otero, K. O. Olasupo, and I. Kostanic, “Empirical path loss models for low power wireless sensor nodes deployed on the ground in different terrains,” in 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2017, pp. 1–8, doi: 10.1109/AEECT.2017.8257747. [54] T. S. Rappaport, Wireless Communications: Principles and practice, Segunda ed. New Jersey: Prentice Hall, 2002. [55] C. Phillips, D. Sicker, and D. Grunwald, “A survey of wireless path loss prediction and coverage mapping methods,” IEEE Commun. Surv. Tutorials, vol. 15, no. 1, pp. 255–270, 2013, doi: 10.1109/SURV.2012.022412.00172. [56] H. S. Jo, C. Park, E. Lee, H. K. Choi, and J. Park, “Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network and gaussian process,” Sensors (Switzerland), vol. 20, no. 7, 2020, doi: 10.3390/s20071927. [57] A. Burkov, The Hundred-Page Machine Learning Book. 2019. [58] K. Murphy, Machine learning : a probabilistic perspective. Cambridge, Massachusetts: The MIT Press, 2012. [59] G. S. Kori and M. S. Kakkasageri, “Classification And Regression Tree (CART) based resource allocation scheme for Wireless Sensor Networks,” Comput. Commun., vol. 197, no. June 2022, pp. 242–254, 2023, doi: 10.1016/j.comcom.2022.11.003. [60] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, pp. 7–8, 2019, doi: 10.3390/app9091908. [61] A. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput., vol. 14, no. 1, pp. 199– 222, 2004, doi: 10.1186/s12984-021-00806-6. [62] A. Ghatak, Machine Learning with R. Singapur: Springer, 2017. [63] O. Theobald, Machine Learning For Absolute Beginners, Segunda. Scatterplot Press, 2017. [64] H. Kumar Gianey and R. Choudhary, “Comprehensive Review On Supervised Machine Learning Algorithms,” in 2017 International Conference on Machine learning and Data Science, 2017, pp. 38–43, doi: 10.1109/MLDS.2017.11. [65] G. Chitralekha and J. M. Roogi, “A Quick Review of ML Algorithms,” in Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021, 2021, pp. 1–5, doi: 10.1109/ICCES51350.2021.9488982. [66] P. Probst, M. N. Wright, and A. L. Boulesteix, “Hyperparameters and tuning strategies for random forest,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 9, no. 3, pp. 1–15, 2019, doi: 10.1002/widm.1301. [67] L. Breiman, “Random Forest,” Mach Learn, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1007/978-3-030-62008- 0_35. [68] N. Moraitis, L. Tsipi, D. Vouyioukas, A. Gkioni, and S. Louvros, “Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz,” Wirel. Networks, vol. 27, no. 6, pp. 4169– 4188, 2021, doi: 10.1007/s11276-021-02682-3. [69] A.-L. Boulesteix, S. Janitza, J. Kruppa, and I. R. Konig, “Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics,” WIREs Data Min. Knowl. Discov., vol. 2, no. 6, pp. 493–507, 2012, doi: https://doi.org/10.1002/widm.1072. [70] A. Ziegler and I. R. König, “Mining data with random forests: current options for real-world applications,” WIREs Data Min. Knowl. Discov., vol. 4, no. 1, pp. 55–63, 2013, doi: https://doi.org/10.1002/widm.1114. [71] M. Kuhn and K. Johnson, Applied Predictive Modeling. New York: Springer, 2013. [72] S. Muthukumaran, P. Geetha, and E. Ramaraj, “Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model,” Intell. Autom. Soft Comput., vol. 35, no. 1, pp. 215–230, 2023, doi: https://doi.org/10.32604/iasc.2023.027449. [73] N. N. Mrabti et al., “Molecular Docking and QSAR Studies for Modeling the Inhibitory Activity of Pyrazolebenzimidazolone Hybrids as Novel Inhibitors of Human 4-hydroxyphenylpyruvate dioxygenase Against Type I Tyrosinemia Disease,” Biointerface Res. Appl. Chem., vol. 13, no. 1, pp. 1–17, 2023, doi: 8 https://doi.org/10.33263/BRIAC131.038. [74] G. Sarker, “A Survey on Convolution Neural Networks,” in 2020 IEEE Region 10 Conference (TENCON), 2020, pp. 923–928, doi: 10.1109/TENCON50793.2020.9293902. [75] Minciencias, “Cuadro tipología de productos,” 2020. . [76] S. Kurt and B. Tavli, “Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations,” Ieee Antennas Propag. Mag., vol. 59, no. 1, pp. 18–37, 2017, doi: 10.1109/MAP.2016.2630035. [77] L. M. Kamarudin, R. B. Ahmad, B. L. Ong, F. Malek, A. Zakaria, and M. A. M. Arif, “Review and modeling of vegetation propagation model for wireless sensor networks using OMNeT++,” in Proceedings - 2nd International Conference on Network Applications, Protocols and Services, NETAPPS 2010, 2010, pp. 78–83, doi: 10.1109/NETAPPS.2010.21. [78] A. Kochhar and N. Kumar, “Wireless sensor networks for greenhouses: An end-to-end review,” Comput. Electron. Agric., vol. 163, no. July, 2019, doi: 10.1016/j.compag.2019.104877. [79] R. Anzum et al., “A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145397. [80] M. Sander-Frigau et al., “A Measurement Study of TVWS Wireless Channels in Crop Farms,” Proc. - 2021 IEEE 18th Int. Conf. Mob. Ad Hoc Smart Syst. MASS 2021, pp. 344–354, 2021, doi: 10.1109/MASS52906.2021.00051. [81] D. Cama-Pinto, J. A. Holgado-Terriza, M. Damas-Hermoso, F. Gómez-Mula, and A. Cama-Pinto, “RadioWave Attenuation Measurement System Based on RSSI for Precision Agriculture: Application to Tomato Greenhouses,” Inventions, vol. 6, no. 4, p. 66, 2021, doi: https://doi.org/10.3390/inventions6040066. [82] L. Juan-Llacer, D. P. Riquelme, J. M. Molina-Garcia-Pardo, J. V. Rodriguez, M. T. Martinez-Ingles, and J. Pascual-Garcia, “A Simplified Model for Path Loss Estimation in Citrus Plantations at 3.5 GHz,” IEEE Antennas Wirel. Propag. Lett., vol. 21, no. 6, pp. 1183–1187, 2022, doi: 10.1109/LAWP.2022.3161098. [83] D. Cama-Pinto et al., “Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions,” Sensors, vol. 20, no. 22, pp. 1–18, 2020, doi: 10.3390/s20226621. [84] P. Avila-Campos, F. Astudillo-Salinas, and A. Vazquez-Rodas, “Evaluation of LoRaWAN Transmission Range for Wireless Sensor Networks in Riparian Forests,” in MSWiM 2019 - Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2019, pp. 199–206, doi: https://doi.org/10.1145/3345768.3355934. [85] O. Artemenko, A. Rubina, A. H. Nayak, S. Baptist, and A. Mitschele-thiel, “Evaluation of di ff erent signal propagation models for a mixed indoor-outdoor scenario using empirical data,” EAI Endorsed Trans. Mob. Commun. Appl., vol. 2, no. 7, pp. 1–8, 2016, doi: 10.4108/eai.20-6-2016.151519. [86] N. Devarajan and S. H. Gupta, “Implementation and Analysis of Different Path Loss Models for Cooperative Communication,” Smart Innov. Commun. Comput. Sci. Adv. Intell. Syst. Comput., vol. 851, 2019, doi: https://doi.org/10.1007/978-981-13-2414-7_22. [87] C.-X. Wang, J. Bian, J. Sun, W. Zhang, and M. Zhang, “A Survey of 5G Channel Measurements and Models,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 3142–3168, 2018, doi: 10.1109/COMST.2018.2862141. [88] S. Duangsuwan, P. Juengkittikul, and M. Myint Maw, “Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios,” Int. J. Antennas Propag., vol. 2021, 2021, doi: 10.1155/2021/5524709. [89] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091908. [90] G. Vergos, S. P. Sotiroudis, G. Athanasiadou, G. V. Tsoulos, and S. K. Goudos, “Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction,” in 2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021, 2021, pp. 1–4, doi: 10.1109/MOCAST52088.2021.9493374. [91] C. A. Oroza, Z. Zhang, T. Watteyne, and S. D. Glaser, “A Machine-Learning-Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, pp. 576–584, 2017, doi: 10.1109/TCCN.2017.2741468. [92] S. K. Goudos, G. Athanasiadou, G. V. Tsoulos, and V. Rekkas, “Modelling Ray Tracing Propagation Data Using Different Machine Learning Algorithms,” 14th Eur. Conf. Antennas Propagation, EuCAP 2020, 2020, doi: 10.23919/EuCAP48036.2020.9135639. [93] M. K. Elmezughi, O. Salih, T. J. Afullo, and K. J. Duffy, “Comparative Analysis of Major Machine-LearningBased Path Loss Models for Enclosed Indoor Channels,” Sensors, vol. 22, no. 13, pp. 1–25, 2022, doi: 10.3390/s22134967. [94] A. L. Barcellos, J. C. Duarte, and A. C. Mendes, “Radiofrequency Signal Levels Predition Using Machine Learning Models,” IEEE Lat. Am. Trans., vol. 21, no. 2, pp. 351–357, 2023, doi: 10.1109/TLA.2023.10015229. [95] O. J. Famoriji and T. Shongwe, “Path Loss Prediction in Tropical Regions using Machine Learning Techniques: A Case Study,” Electron., vol. 11, no. 17, pp. 1–13, 2022, doi: 10.3390/electronics11172711. [96] N. Kuno, W. Yamada, M. Inomata, M. Sasaki, Y. Asai, and Y. Takatori, “Evaluation of characteristics for NN and CNN in path loss prediction,” in 2020 International Symposium on Antennas and Propagation, ISAP 2020, 2021, pp. 61–62, doi: 10.23919/ISAP47053.2021.9391493. [97] R. O. Abolade, S. O. Famakinde, S. I. Popoola, O. F. Oseni, A. A. Atayero, and S. Misra, “Support Vector Machine for Path Loss Predictions in Urban Environment,” in ICCSA 2020: Computational Science and Its Applications. Lecture Notes in Computer Science, 2020, vol. 12255, pp. 995–1006, doi: 10.1007/978-3-030- 58820-5_71. [98] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure,” IEEE Trans. Ind. Informatics, vol. 18, no. 10, pp. 6981–6990, 2022, doi: 10.1109/TII.2022.3142318. [99] C. E. García and I. Koo, “Extremely Randomized Trees Regressor Scheme for Mobile Network Coverage Prediction and REM Construction,” IEEE Access, vol. 11, no. July, pp. 65170–65180, 2023, doi: 10.1109/ACCESS.2023.3287103. [100] K. Kayaalp, S. Metlek, A. Genc, H. Dogan, and İ. B. Basyigit, “Prediction of path loss in coastal and vegetative environments with deep learning at 5G sub-6 GHz,” Wirel. Networks, vol. 0123456789, pp. 2471–2480, 2023, doi: 10.1007/s11276-023-03285-w. [101] M. González-Palacio, D. Tobón-Vallejo, L. Sepúlveda-Cano, S. Rúa, and L. Bao Le, “Machine-Learning-Based Combined Path Loss and Shadowing Model in LoRaWAN for Energy Efficiency Enhancement,” EEE Internet Things J., vol. 10, no. 12, pp. 10725–10739, 2023, doi: 10.1109/JIOT.2023.3239827. [102] R. T. Juang, “Path loss modelling based on path profile in urban propagation environments,” IET Commun., vol. 16, no. 6, pp. 685–694, 2022, doi: 10.1049/cmu2.12369. [103] K. J. Jang et al., “Path Loss Model Based on Machine Learning Using Multi-Dimensional Gaussian Process Regression,” IEEE Access, vol. 10, no. November, pp. 115061–115073, 2022, doi: 10.1109/ACCESS.2022.3217912. [104] H. M. Jawad et al., “Accurate Empirical Path-Loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture,” IEEE Sens. J., vol. 20, no. 1, pp. 552–561, 2020, doi: 10.1109/JSEN.2019.2940186. [105] H. Wu, Y. Miao, F. Li, and L. Zhu, “Empirical modeling and evaluation of multi-path radio channels on wheat farmland based on communication quality,” Trans. ASABE, vol. 59, no. 3, pp. 759–767, 2016, doi: 10.13031/trans.59.11016. [106] D. Cama-Pinto, M. Damas, J. A. Holgado-Terriza, F. Gómez-Mula, and A. Cama-Pinto, “Path loss determination using linear and cubic regression inside a classic tomato greenhouse,” Int. J. Environ. Res. Public Health, vol. 16, no. 10, p. 1744, 2019, doi: https://doi.org/10.3390/ijerph16101744. [107] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “NSGA-III Based Heterogeneous Transmission Range Selection for Node Deployment in IEEE 802.15.4 Infrastructure for Sugarcane and Rice Crop Monitoring in a Humid Sub-Tropical Region,” IEEE Trans. Wirel. Commun., vol. 22, no. 6, pp. 3643–3656, 2023, doi: 10.1109/TWC.2022.3220146. [108] S. Phaiboon and P. Phokharatkul, “Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression,” Sensors, vol. 23, no. 7, pp. 1–20, 2023, doi: 10.3390/s23073525. [109] N. Leonor, S. Faria, M. Vala, and R. F. S. Caldeirinha, “A Combined ITM and LITU-R Model for Enhanced Radio Coverage Predictions of Mission-Critical Communications in Mountainous Vegetated Terrains,” IEEE Antennas Wirel. Propag. Lett., vol. 21, no. 9, pp. 1777–1781, 2022, doi: 10.1109/LAWP.2022.3179625. [110] G. Hakim et al., “Near Ground Pathloss Propagation Model Using Adaptive Communication in Forest , Jungle and Open Dirt,” Sensors, vol. 22, no. 3267, 2022, doi: 10.3390/s22093267. [111] Y. Voutos, P. Mylonas, E. Spyrou, and E. Charou, “An IoT-based insular monitoring architecture for smart viticulture,” 2019, doi: 10.1109/IISA.2018.8633630. [112] N. Karimi, A. Arabhosseini, M. Karimi, and M. H. Kianmehr, “Web-based monitoring system using Wireless Sensor Networks for traditional vineyards and grape drying buildings,” Comput. Electron. Agric., vol. 144, no. December 2016, pp. 269–283, 2018, doi: 10.1016/j.compag.2017.12.018. [113] F. Muzafarov and A. Eshmuradov, “Wireless sensor network based monitoring system for precision agriculture in Uzbekistan,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 3, pp. 1071–1080, 2019, doi: 10.12928/TELKOMNIKA.V17I3.11513. [114] S. Phaiboon and P. Phokharatkul, “An Empirical Model For 433 MHz LoRa-WAN in Ruby Mango Plantation,” in 2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST), 2023, no. 3, pp. 25–28, doi: 10.1109/iceast58324.2023.10157266. [115] T. H. Nasution, M. Yasir, Fahmi, and Soeharwinto, “Designing an IoT system for monitoring and controlling temperature and humidity in mushroom cultivation fields,” in ICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding, 2019, pp. 326–331, doi: 10.1109/ICECOS47637.2019.8984446. [116] N. Kaur and G. Deep, “IoT-Based Brinjal Crop Monitoring System,” in Smart Sensors for Industrial Internet of Things, 2021. [117] E. P. Kho, S. N. D. Chua, S. F. Lim, L. C. Lau, and M. T. N. Gani, “Development of young sago palm environmental monitoring system with wireless sensor networks,” Comput. Electron. Agric., vol. 193, no. September 2021, p. 106723, 2022, doi: 10.1016/j.compag.2022.106723. [118] L. Yu et al., “Assessment of cornfield LAI retrieved from multi-source satellite data using continuous field LAI measurements based on a wireless sensor network,” Remote Sens., vol. 12, no. 20, pp. 1–19, 2020, doi: 10.3390/rs12203304. [119] A. Zabasta, A. Avotins, R. Porins, P. Apse-Apsitis, J. Bicans, and D. Korabicka, “Development of IoT based Monitoring and Control System for Small Industrial Greenhouses,” in 2021 10th Mediterranean Conference on Embedded Computing, MECO 2021, 2021, pp. 7–10, doi: 10.1109/MECO52532.2021.9460230. [120] A. Touhami, K. Benahmed, and F. Bounaama, “Monitoring of Greenhouse Based on Internet of Things and Wireless Sensor Network,” 2020, doi: https://doi-org.ezproxy.cuc.edu.co/10.1007/978-3-030-21009-0_27. [121] D. P. Rubanga, K. Hatanaka, and S. Shimada, “Development of a simplified smart agriculture system for smallscale greenhouse farming,” Sensors Mater., vol. 31, no. 3, pp. 831–843, 2019, doi: 10.18494/SAM.2019.2154. [122] K. P. Ferentinos, N. Katsoulas, A. Tzounis, T. Bartzanas, and C. Kittas, “Wireless sensor networks for greenhouse climate and plant condition assessment,” Biosyst. Eng., vol. 153, pp. 70–81, 2017, doi: 10.1016/j.biosystemseng.2016.11.005. [123] S. Kaur and Deepali, “An automatic irrigation system for different crops with WSN,” in 2017 6th International Conference on Reliability, Infocom Technologies and Optimization: Trends and Future Directions, ICRITO 2017, 2018, vol. 2018-Janua, pp. 406–411, doi: 10.1109/ICRITO.2017.8342460. [124] Y. Mohanraj, V. . Gokul, R. Ezhilarasie, and U. Umamakeswari, “Intelligent Drip Irrigation and Fertigation Using,” in 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2017, pp. 36–41, doi: 10.1109/TIAR.2017.8273682. [125] R. Khan, I. Ali, M. Zakarya, M. Ahmad, M. Imran, and M. Shoaib, “Technology-Assisted Decision Support System for Efficient Water Utilization: A Real-Time Testbed for Irrigation Using Wireless Sensor Networks,” IEEE Access, vol. 6, pp. 25686–25697, 2018, doi: 10.1109/ACCESS.2018.2836185. [126] N. Penchalaiah, J. Nelson Emmanuel, S. Suraj Kamal, and K. Ramana, “IoT Based Automatic Irrigation System Using Wireless Sensor Networks,” in Lecture Notes in Electrical Engineering, 2021. [127] I. Picallo et al., “A radio channel model for D2D communications blocked by single trees in forest environments,” Sensors, vol. 19, no. 21, p. 4606, 2019, doi: https://doi.org/10.3390/s19214606. [128] F. Correia Pinheiro, M. Sampaio De Alencar, W. T. Araújo Lopes, M. Soares De Assis, and B. Gonçalves Leal, “Propagation analysis for wireless sensor networks applied to viticulture,” Int. J. Antennas Propag., vol. 2017, 2017, doi: https://doi.org/10.1155/2017/7903839 Research. [129] Q. M. Qadir, T. A. Rashid, N. K. Al-Salihi, B. Ismael, A. A. Kist, and Z. Zhang, “Low power wide area networks: A survey of enabling technologies, applications and interoperability needs,” IEEE Access, vol. 6, pp. 77454–77473, 2018, doi: 10.1109/ACCESS.2018.2883151. [130] Lora Alliance, “What is LoRaWAN Specification,” What is LoRaWAN, 2021. https://lora-alliance.org/aboutlorawan/ (accessed Feb. 01, 2021). [131] J. De Carvalho Silva, J. J. P. C. Rodrigues, A. M. Alberti, P. Solic, and A. L. L. Aquino, “LoRaWAN - A low power WAN protocol for Internet of Things: A review and opportunities,” in 2017 2nd International Multidisciplinary Conference on Computer and Energy Science, SpliTech 2017, 2017, pp. 1–6. [132] A. Lombardo, S. Parrino, G. Peruzzi, and A. Pozzebon, “LoRaWAN Versus NB-IoT: Transmission Performance Analysis Within Critical Environments,” IEEE Internet Things J., vol. 9, no. 2, pp. 1068–1081, 2022, doi: 10.1109/JIOT.2021.3079567. [133] H. Sharma and S. Sharma, “A review of sensor networks: Technologies and applications,” in 2014 Recent Advances in Engineering and Computational Sciences, RAECS 2014, 2014, pp. 6–8, doi: 10.1109/RAECS.2014.6799579. [134] S. Al-sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, “Review,” pp. 685–690, 2017. [135] W. Tang, X. Ma, J. Wei, and Z. Wang, “Measurement and analysis of near-ground propagation models under different terrains for wireless sensor networks,” Sensors, vol. 19, no. 8, p. 1901, 2019, doi: https://doi.org/10.3390/s19081901. [136] H. Wu, L. Zhang, and Y. Miao, “The Propagation Characteristics of Radio Frequency Signals for Wireless Sensor Networks in Large-Scale Farmland,” Wirel. Pers. Commun., vol. 95, no. 4, pp. 3653–3670, 2017, doi: 10.1007/s11277-017-4018-5. [137] T. Rama Rao, D. Balachander, and N. Tiwari, “RF Propagation Measurements in Forest & Plantation Environments for Wireless Sensor Networks,” in 2012 IEEE International Conference on Communication Systems, ICCS 2012, 2012, pp. 194–198, doi: 10.1109/ICCS.2012.6406137 [138] H. T. Anastassiu et al., “A computational model for path loss in wireless sensor networks in orchard environments,” Sensors, vol. 14, no. 3, pp. 5118–5135, 2014, doi: https://doi.org/10.3390/s140305118. [139] D. L. Ndzi et al., “Wireless sensor network coverage measurement and planning in mixed crop farming,” Comput. Electron. Agric., vol. 105, pp. 83–94, 2014, doi: 10.1016/j.compag.2014.04.012. [140] J. A. Azevedo and F. E. Santos, “A model to estimate the path loss in areas with foliage of trees,” AEU - Int. J. Electron. Commun., vol. 71, pp. 157–161, 2017, doi: 10.1016/j.aeue.2016.10.018. [141] X. ming Guo, X. ting Yang, M. xiang Chen, M. Li, and Y. an Wang, “A model with leaf area index and apple size parameters for 2.4 GHz radio propagation in apple orchards,” Precis. Agric., vol. 16, no. 2, pp. 180–200, 2015, doi: 10.1007/s11119-014-9369-2. [142] T. O. Olasupo and C. E. Otero, “The Impacts of Node Orientation on Radio Propagation Models for AirborneDeployed Sensor Networks in Large-Scale Tree Vegetation Terrains,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 50, no. 1, pp. 256–269, 2020, doi: 10.1109/TSMC.2017.2737473. [143] Z. Gao et al., “Wireless channel propagation characteristics and modeling research in rice field sensor networks,” Sensors, vol. 18, no. 9, p. 3116, 2018, doi: https://doi.org/10.3390/s18093116. [144] A. Raheemah, N. Sabri, M. S. Salim, P. Ehkan, and R. B. Ahmad, “New empirical path loss model for wireless sensor networks in mango greenhouses,” Comput. Electron. Agric., vol. 127, pp. 553–560, 2016, doi: 10.1016/j.compag.2016.07.011. [145] G. P. N. Hakim, M. Alaydrus, and R. B. Bahaweres, “Empirical approach of ad hoc path loss propagation model in realistic forest environments,” Proceeding - 2016 Int. Conf. Radar, Antenna, Microwave, Electron. Telecommun. ICRAMET 2016, pp. 139–143, 2017, doi: 10.1109/ICRAMET.2016.7849600. [146] N. Shutimarrungson and P. Wuttidittachotti, “Realistic propagation effects on wireless sensor networks for landslide management,” EURASIP J. Wirel. Commun. Netw., vol. 94, 2019, doi: https://doi.org/10.1186/s13638-019-1412-6. [147] J. A. Gay-Fernández and I. Cuiñas, “Peer to peer wireless propagation measurements and path-loss modeling in vegetated environments,” IEEE Trans. Antennas Propag., vol. 61, no. 6, pp. 3302–3311, 2013, doi: 10.1109/TAP.2013.2254452. [148] Y. Rao, Z. hui Jiang, and N. Lazarovitch, “Investigating signal propagation and strength distribution characteristics of wireless sensor networks in date palm orchards,” Comput. Electron. Agric., vol. 124, pp. 107– 120, 2016, doi: 10.1016/j.compag.2016.03.023. [149] A. AlSayyari, I. Kostanic, and C. E. Otero, “An Empirical Path Loss Model for Wireless Sensor Network Deployment in a Dense Tree Environment,” in 2017 IEEE Sensors Applications Symposium (SAS), 2017, pp. 1–6, doi: 10.1109/SAS.2017.7894099. [150] A. Navarro, D. Guevara, and G. A. Florez, “An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields,” in 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020, no. September, pp. 1–3, doi: 10.23919/URSIGASS49373.2020.9232365. [151] T. Hamasaki, “Propagation Characteristics of A 2.4GHz Wireless Sensor Module with A Pattern Antenna in Forestry and Agriculture Field,” in 2019 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), 2019, pp. 1–3, doi: 10.1109/RFIT.2019.8929207. [152] A. Alsayyari and A. Aldosary, “Path loss results for wireless sensor network deployment in a sparse tree environment,” 2019 Int. Symp. Networks, Comput. Commun., vol. 1–6, pp. 1–6, 2019, doi: 10.1109/ISNCC.2019.8909137. [153] X. Xu et al., “Measurement and Analysis of Wireless propagative Model of 433MHz and 2.4GHz Frequency in Southern China Orchards,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 695–699, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.08.115. [154] DIGI, “Digi XBee 3 Zigbee Mesh Kit, worldwide,” 2023. https://www.digi.com/products/models/xk3-z8s-wzm (accessed Oct. 21, 2023). [155] A. Barrios-Ulloa, P. P. Ariza-Colpas, H. Sánchez-Moreno, A. P. Quintero-Linero, and E. De la Hoz-Franco, “Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145285. [156] Agencia Nacional del Espectro, “Espectro para atender el crecimiento futuro y la masificación de aplicaciones IoT,” 2022. https://www.ane.gov.co/Sliders/archivos/gestión técnica/Estudios de gestión y planeación/Espectro para IoT/Documentos para consulta/DocumentoEspectroIoT.pdf (accessed Oct. 21, 2023). [157] Agencia Nacional del Espectro, “DOCUMENTO DE CONSULTA PÚBLICA SOBRE LAS BANDAS DE FRECUENCIAS DISPONIBLES PARA EL FUTURO DESARROLLO DE LAS TELECOMUNICACIONES MÓVILES INTERNACIONALES (IMT) EN COLOMBIA,” 2020. https://www.ane.gov.co/Documentos compartidos/ArchivosDescargables/noticias/Consulta pública sobre las bandas disponibles para el futuro desarrollo de las IMT en Colombia.pdf (accessed Oct. 21, 2023). [158] Agencia Nacional del Espectro, “Consulta pública para la banda de 900 MHz,” 2021. https://www.ane.gov.co/Sliders/archivos/gestión técnica/Estudios de gestión y planeación/900 MHz/Documentos/Consulta publica banda de 900 MHz .pdf (accessed Oct. 21, 2023). [159] M. Aguilera Díaz, “Documentos de Trabajo Sobre Economía Regional,” 2012. [Online]. Available: https://www.banrep.gov.co/sites/default/files/publicaciones/archivos/dtser_158.pdf. [160] D. Álvarez Arroyo, “Entrevista técnica privada,” 2022. [161] H. Dogan, “A new empirical propagation model depending on volumetric density in citrus orchards for wireless sensornetwork applications at sub-6 GHz frequency region,” Int. J. RF Microw. Comput. Eng., vol. 31, no. 9, p. e22778, 2021, doi: https://doi.org/10.1002/mmce.22778. [162] P. Phokharatkul and S. Phaiboon, “Path Loss Model for the Bananas and Weeds Environment Based on Grey System Theory,” in 2021 Photonics & Electromagnetics Research Symposium (PIERS), 2021, pp. 413–418, doi: 10.1109/PIERS53385.2021.9694777. [163] K. Kayaalp, S. Metlek, A. Genc, H. Dogan, and B. B. Bahadir Basyigitİbrahim, “Prediction of path loss in coastal and vegetative environments with deep learning at 5G sub-6 GHz,” Wirel. Networks, vol. 29, pp. 2471– 2480, 2023, doi: https://doi.org/10.1007/s11276-023-03285-w. [164] D. Cama-Pinto, “Modelos Empíricos de Radio Propagación en Presencia de Vegetación para Aplicaciones Agrícolas,” Universidad de Granada, 2022. [165] L. Juan-Llacer et al., “Path Loss Measurements and Modelling in a Citrus Plantation in the 1800 MHz, 3.5 GHz and 28 GHz in LoS,” 2022 16th Eur. Conf. Antennas Propagation, EuCAP 2022, no. 1, 2022, doi: 10.23919/eucap53622.2022.9769016. [166] H. Pan, Y. Shi, X. Wang, and T. Li, “Modeling wireless sensor networks radio frequency signal loss in corn environment,” Multimed. Tools Appl., vol. 76, no. 19, pp. 19479–19490, 2017, doi: https://doi.org/10.1007/s11042-015-3150-z. [167] S. Khalid, T. Khalil, and S. Nasreen, “A survey of feature selection and feature extraction techniques in machine learning,” in Proceedings of 2014 Science and Information Conference, SAI 2014, 2014, no. October, pp. 372– 378, doi: 10.1109/SAI.2014.6918213. [168] A. Barrios-Ulloa, A. Cama-Pinto, E. De-la-Hoz-Franco, R. Velarde-Ramírez, and D. Cama-Pinto, “Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning,” Agric., vol. 13, no. 11, p. 2046, 2023, doi: 10.3390/agriculture13112046. [169] O. Theoblad, Machine Learning For Absolute Beginners, Segunda. Scatterplot Press, 2014. [170] E. Alpaydın, Introduction to Machine Learning, Tercera. Cambridge, Massachusetts: The MIT Press, 2014. [171] ITU-R, “Recommendation ITU-R P.1411-12,” 2023. https://www.itu.int/dms_pubrec/itu-r/rec/p/R-RECP.1411-12-202308-I!!PDF-E.pdf (accessed Mar. 26, 2023). [172] J. Zhang, L. Liu, Y. Fan, L. Zhuang, T. Zhou, and Z. Piao, “Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning,” IEEE Access, vol. 8, pp. 47797–47806, 2020, doi: 10.1109/ACCESS.2020.2979220. [173] A. Kochhar, N. Kumar, and U. Arora, “Signal Assessment Using ML for Evaluation of WSN Framework in greenhouse monitoring,” Int. J. Sensors , Wirel. Commun. Control, vol. 12, no. 09, pp. 669–679, 2022, doi: 0.2174/2210327913666221220154338. [174] T. Nagao and T. Hayashi, “Fine-Tuning for Propagation Modeling of Different Frequencies with Few Data,” IEEE Veh. Technol. Conf., vol. 2022-Septe, pp. 1–5, 2022, doi: 10.1109/VTC2022-Fall57202.2022.10012911. [175] S. Wu, B. Ma, T. Ye, J. Zhang, W. Shao, and W. Zheng, “A Machine Learning based Intelligent Propagation Model for RSRP prediction,” in Proceedings - 2022 International Seminar on Computer Science and Engineering Technology, SCSET 2022, 2022, pp. 1–5, doi: 10.1109/SCSET55041.2022.00010. [176] S. K. Goudos, G. Athanasiadou, G. V. Tsoulos, and V. Rekkas, “Modelling Ray Tracing Propagation Data Using Different Machine Learning Algorithms,” 2020, doi: 10.23919/EuCAP48036.2020.9135639. [177] S. Ojo, A. Sari, and T. P. Ojo, “Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models,” Open J. Appl. Sci., vol. 12, no. 06, pp. 990–1010, 2022, doi: 10.4236/ojapps.2022.126068.
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
dc.rights.uri.spa.fl_str_mv	https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv	http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	116 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Corporación Universidad de la Costa
dc.publisher.department.spa.fl_str_mv	Ciencias de la Computación y Electrónica
dc.publisher.place.spa.fl_str_mv	Barranquilla, Colombia
dc.publisher.program.spa.fl_str_mv	Doctorado en Tecnologías de la Información y la Comunicación
institution	Corporación Universidad de la Costa
bitstream.url.fl_str_mv	https://repositorio.cuc.edu.co/bitstreams/bc5c2d15-e1b2-4407-b4d4-2ac46aa120ad/download https://repositorio.cuc.edu.co/bitstreams/d9b44105-a8f4-41c6-bdde-59545a85ddfe/download https://repositorio.cuc.edu.co/bitstreams/69a40a17-3bec-4b9a-bf2b-46973d9d7190/download https://repositorio.cuc.edu.co/bitstreams/a1561b10-c2c1-43a7-8632-e5e06d846d99/download
bitstream.checksum.fl_str_mv	8d31ced6d5d458b9de867dc49b10fd6b 2f9959eaf5b71fae44bbf9ec84150c7a f1b29901ab48de044a16cf7e7ab2b22a ac38601ac0e90a5dd6c0569322d8b0f7
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv	repdigital@cuc.edu.co
_version_	1828166760946204672
spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cama-Pinto, AlejandroDe-La-Hoz-Franco, EmiroBarrios-Ulloa, AlexisSalcedo Morillo, DixonSilva Cárdenas, CarlosSierra Carrillo, Javier2024-07-04T15:53:35Z2024-07-04T15:53:35Z2024https://hdl.handle.net/11323/13114Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/El modelado de la propagación de ondas en un canal de telecomunicaciones es una de las tareas más importantes para los ingenieros encargados de la planificación de redes inalámbricas, dentro de las cuales se encuentrasn las redes inalámbricas de sensores. De la eficiencia de los modelos usados depende en gran medida que los receptores puedan hacer una estimación adecuada del mensaje enviado desde el transmisor. Tradicionalmente, los modelos de pérdida de ruta desarrollados han sido principalmente empíricos o deterministas, con una menor porción de estocásticos. No obstante, debido a la búsqueda continua de los investigadores de métodos que mejoren la predicción de la pérdida de ruta, en la última década ha ganado fuerza el uso de Machine Learning como herramienta para reducir los niveles de error en la estimación de la atenuación. A pesar de ello, las investigaciones se concentran casi en su totalidad en el modelado para redes de telefonía móvil en entornos urbanos y suburbanos, en dejando de lado el estudio en otros entornos que pueden ser igualmente complicados y desafiantes, tal es el caso de aquellos con presencia de vegetación considerable, tal es el caso de las plantaciones agrícolas, donde el uso de sistemas de monitoreo basados en redes inalámbricas de sensores está cobrando cada vez más fuerza. Esta tesis doctoral presenta los resultados del modelamiento de las pérdidas de ruta causada por los arbustos en cultivos de yuca, considerados uno de los cultivos de mayor importancia para la sostenibilidad alimenticia en diferentes países. A pesar de su potencial, hay pocos reportes de investigaciones relacionadas con implementación de tecnologías de la información y la comunicación en este tipo de sembrados. La disertación se basa en la evaluación del rendimiento de algoritmos de Machine Learning que han sido entrenados utilizando mediciones reales. Como resultado, los modelos obtenidos mejoran significativamente la predicción de la pérdida de ruta en comparación con los resultados de los modelos de vegetación canónicos, alcanzando valores de error medio cuadrático (RMSE), error medio aritmético (MAE) por debajo de los 4 dB y coeficiente de determinación por encima del 98%.Modelling wave propagation in a telecommunications channel is one of the most important tasks for engineers responsible for wireless network planning, which includes wireless sensor networks. The efficiency of the models used greatly determines whether receivers can make an accurate estimation of the message sent from the transmitter. Traditionally, the path loss models developed have been primarily empirical or deterministic, with a smaller portion being stochastic. However, due to the constant search by researchers for methods to improve route loss prediction, the use of Machine Learning to reduce error levels in attenuation estimation has gained momentum in the last decade. Despite this, research is almost entirely focused on modelling for mobile networks in urban and suburban environments, neglecting the study of other environments that can be equally complex and challenging, such as those with considerable vegetation. This includes agricultural plantations, where the use of monitoring systems based on wireless sensor networks is gaining increasing traction. This doctoral thesis presents the results of path loss modelling caused by cassava bushes, considered one of the most important crops for food sustainability in various countries. Despite their significance, there are few reports of research related to the implementation of information and communication technologies in this type of cultivation. As a result, the models obtained significantly improve the prediction of path loss compared to the results of canonical vegetation models, achieving root mean square error (RMSE) and mean absolute error (MAE) values below 4 dB, and a coefficient of determination above 98%.LISTA DE TABLAS 11 -- LISTA DE FIGURAS 12 -- I. INTRODUCCIÓN 14 -- A. MOTIVACIÓN Y/O PROBLEMA 14 -- B. OBJETIVOS 16 -- C. FUNDAMENTOS 17 -- 1) CULTIVOS DE YUCA 17 -- 2) AGRICULTURA DE PRECISIÓN Y AGRICULTURA 4.0. 19 -- 3) REDES DE SENSORES INALÁMBRICOS 21 -- 4) MODELOS DE PROPAGACIÓN DE ONDAS DE RADIO 22 -- 5) MACHINE LEARNING 24 -- D. CONTRIBUCIONES Y PUBLICACIONES 28 -- E. ORGANIZACIÓN DEL DOCUMENTO 30 -- II. REVISIÓN DE LA LITERATURA 32 -- A. METODOLOGÍA EMPLEADA EN LA RSL 33 -- B. ANÁLISIS TÉCNICO 34 -- C. RESULTADOS DE LA RSL 39 -- D. CONCLUSIONES DE LA RSL 46 -- III. METODOLOGÍA Y ESCENARIOS DE EXPERIMENTACIÓN 47 -- A. EQUIPOS DE MEDICIÓN 47 -- B. PROCEDIMIENTO DE MEDICIÓN 49 -- C. ML EN EL MODELADO DE LA PROPAGACIÓN DE ONDAS DE RADIO EN CULTIVOS 58 -- 1) CONSTRUCCIÓN DEL CONJUNTO DE DATOS 58 -- 2) EXTRACCIÓN DE CARACTERÍSTICAS 58 -- 3) NORMALIZACIÓN DE DATOS 62 -- 4) SELECCIÓN DE MODELOS 62 -- 5) ENTRENAMIENTO DE MODELOS Y SINTONIZACIÓN DE HIPERPARÁMETROS 63 -- 6) VALIDACIÓN Y EVALUACIÓN DE RESULTADOS 63 -- D. CONCLUSIONES DEL CAPÍTULO 65 -- IV. RESULTADOS 67 -- A. COMPARACIÓN DE MODELOS DE VEGETACIÓN 67 -- B. MODELOS DE PROPAGACIÓN DE ML EN CULTIVOS DE YUCA 75 -- 1) MODELO SVR 75 -- 2) MODELO DT 78 -- 3) MODELO RF 80 -- 4) MODELO ANN 83 -- C. VALIDACIÓN CRUZADA 85 -- D. COMPARACIÓN DE RESULTADOS CON INVESTIGACIONES SIMILARES 87 -- E. CONCLUSIONES DEL CAPÍTULO 96 -- V. CONCLUSIONES Y APORTES 98 -- A. CONCLUSIONES 98 -- B. APORTES 99 -- C. TRABAJOS FUTUROS 100 -- D. DISCUSIONES 101 -- REFERENCIAS 103Doctor(a) en Tecnologías de la Información y la ComunicaciónDoctorado116 páginasapplication/pdfspaCorporación Universidad de la CostaCiencias de la Computación y ElectrónicaBarranquilla, ColombiaDoctorado en Tecnologías de la Información y la ComunicaciónModelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yucaTrabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06Textinfo:eu-repo/semantics/doctoralThesishttp://purl.org/redcol/resource_type/TDinfo:eu-repo/semantics/acceptedVersion[1] ONU, “World Population Prospects 2022,” 2022. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of _results.pdf (accessed Mar. 23, 2024).[2] FAO, “Seguimiento del progreso en los indicadores de los ODS relacionados con la alimentación y la agricultura 2022,” Roma, 2022.[3] UNDP, “The SDGS In action,” 2022. https://www.undp.org/sustainable-developmentgoals?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_ca mpaign=CENTRAL&c_src=CENTRAL&c_src2=GSR&gclid=EAIaIQobChMIv8W0rCC9wIVjhmtBh1Q5gOmEAAYASAAEgKvC_D_BwE (accessed Apr. 07, 2022).[4] FAO, “La agricultura mundial en la perspectiva del año 2050,” Roma, 2009. [Online]. Available: http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/Issues_papers_SP/La_agricultura_mundial.p df.[5] A. Janket et al., “Quantitative evaluation of macro-nutrient uptake by cassava in a tropical savanna climate,” Agric., vol. 11, no. 12, pp. 1–22, 2021, doi: 10.3390/agriculture11121199.[6] R. O. Enesi, S. Hauser, P. Pypers, C. Kreye, M. Tariku, and J. Six, “Understanding changes in cassava root dry matter yield by different planting dates, crop ages at harvest, fertilizer application and varieties,” Eur. J. Agron., vol. 133, p. 126448, 2022, doi: 10.1016/j.eja.2021.126448.[7] G. S. G. Byju, “Mineral nutrition of cassava,” in Advances in Agronomy, 2020, pp. 169–235.[8] FAO, “Cassava’s huge potential as 21st Century crop,” 2013. https://www.fao.org/news/story/en/item/176780/icode/ (accessed Apr. 08, 2022).[9] World Economic Forum, “This root vegetable could help alleviate hunger and end soil erosion. Here’s how,” 2021. https://www.weforum.org/agenda/2021/02/cassava-root-end-soil-erosion-deforestation/ (accessed Aug. 28, 2023).[10] E. A. Rosero Alpala, H. Ceballos Lascano, and E. Rodríguez Henao, “Aportes y perspectivas del mejoramiento genético de yuca para el fortalecimiento de su red de valor en Colombia,” AGROSAVIA, 2023. https://editorial.agrosavia.co/index.php/publicaciones/catalog/view/315/308/1875-1 (accessed Oct. 14, 2023).[11] AGRONET, “Reporte: Área, Producción y Rendimiento Nacional por Cultivo,” Ministerio de Agricultura y Desarrollo Rural, 2024. https://www.agronet.gov.co/estadistica/Paginas/home.aspx?cod=3 (accessed Mar. 23, 2024).[12] Ministerio de Agricultura y Desarrollo Rural, “Cadena productiva de la yuca,” 2021. https://sioc.minagricultura.gov.co/Yuca/Documentos/2021-03-31 Cifras Sectoriales yuca.pdf (accessed Oct. 14, 2023).[13] Economic Research Service, “Population and income drive world food production projections,” 2023. https://www.ers.usda.gov/data-products/chart-gallery/gallery/chart-detail/?chartId=108060 (accessed Jun. 08, 2024).[14] M. Dutta and K. Anand, “Role of Information Communication Technology in Agriculture,” Int. J. Nov. Res. Dev., vol. 8, no. 10, pp. 863–870, 2023.[15] P. Rajkhowa and H. Baumüller, “Assessing the potential of ICT to increase land and labour productivity in agriculture: Global and regional perspectives,” J. Agric. Econ., pp. 477–503, 2024, doi: 10.1111/1477- 9552.12566.[16] GAO, “Precision Agriculture: Benefits and Challenges for Technology Adoption and Use,” 2024. https://www.gao.gov/products/gao-24-105962 (accessed Jun. 07, 2024).[17] M. San Emeterio de la Parte, J. Martínez-Ortega, V. Hernández, and N. Martínez, “Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability,” J. Big Data, vol. 10, no. 52, 2023, doi: 10.1186/s40537-023-00729-0.[18] P. Musa, H. Sugeru, and E. P. Wibowo, “Wireless Sensor Networks for Precision Agriculture: A Review of NPK Sensor Implementations,” Sensors, vol. 24, no. 1, p. 51, 2024, doi: https://doi.org/10.3390/s24010051.[19] L. Sevgi, “Groundwave modeling and simulation strategies and path loss prediction virtual tools,” IEEE Trans. Antennas Propag., vol. 55, no. 6 I, pp. 1591–1598, 2007, doi: 10.1109/TAP.2007.897256.[20] J. Stewart, R. Stewart, and S. Kennedy, “Internet of Things - Propagation Modelling for Precision Agriculture Applications,” in Proceedings of the IEEE International Conference Image Information Processing, 2019, pp. 1–8, doi: 10.1109/ICIIP47207.2019.8985688.[21] P. Moreno-Cadena et al., “Modeling growth, development and yield of cassava: A review,” F. Crop. Res., vol. 267, no. March, p. 108140, 2021, doi: 10.1016/j.fcr.2021.108140.[22] The Nippon Foundation and CIAT, The Cassava Handbook. 2012.[23] C. C. Chen, J. Y. Ba, T. J. Li, C. C. K. Chan, K. C. Wang, and Z. Liu, “EfficientNet: A low-bandwidth IoT image sensor framework for cassava leaf disease classification,” Sensors Mater., vol. 33, no. 11, pp. 4031– 4044, 2021, doi: 10.18494/SAM.2021.3526.[24] FAO, “Cultivos y productos de ganadería,” 2021. https://www.fao.org/faostat/es/#data/QCL/visualize (accessed Aug. 28, 2023).[25] J. Aristizábal and T. Sánchez, “Guía técnica para producción y análisis de almidón de yuca,” Fao, 2007. http://www.fao.org/3/a-a1028s.pdf (accessed Apr. 12, 2022).[26] J. G. Caicedo-Ortiz et al., “Monitoring system for agronomic variables based in WSN technology on cassava crops,” Comput. Electron. Agric., vol. 145, no. August 2017, pp. 275–281, 2018, doi: 10.1016/j.compag.2018.01.004.[27] A. Bula, “Importancia de la Agricultura en el Desarollo Socio-Económico,” Universidad Nacional del Rosario, 2020. Accessed: Apr. 15, 2022. [Online]. Available: https://observatorio.unr.edu.ar/wpcontent/uploads/2020/08/Importancia-de-la-agricultura-en-el-desarrollo-socio-económico.pdf.[28] The World Bank, “Agriculture and Food,” 2023. https://www.worldbank.org/en/topic/agriculture/overview#:~:text=Agriculture can help reduce poverty,and work mainly in farming. (accessed Aug. 28, 2023).[29] D. P. Roberts, N. M. Short, J. Sill, D. K. Lakshman, X. Hu, and M. Buser, “Precision agriculture and geospatial techniques for sustainable disease control,” Indian Phytopathol., vol. 74, no. 2, pp. 287–305, 2021, doi: 10.1007/s42360-021-00334-2.[30] D. K. Rathinam, D. Surendran, A. Shilpa, A. Santhiya Grace, and J. Sherin, “Modern Agriculture Using Wireless Sensor Network (WSN),” in 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 2019, pp. 515–519, doi: 10.1109/ICACCS.2019.8728284.[31] M. E. Mondejar et al., “Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet,” Sci. Total Environ., vol. 794, no. June, 2021, doi: 10.1016/j.scitotenv.2021.148539.[32] V. Saiz-Rubio and F. Rovira-Más, “From smart farming towards agriculture 5.0: A review on crop data management,” Agronomy, vol. 10, no. 2, 2020, doi: 10.3390/agronomy10020207.[33] ISPA, “Precision Ag Definition,” 2018. https://www.ispag.org/about/definition (accessed Apr. 15, 2022).[34] M. A. Dayioğlu and U. Türker, “Digital transformation for sustainable future-agriculture 4.0: A review,” J. Agric. Sci., vol. 27, no. 4, pp. 373–399, 2021, doi: 10.15832/ankutbd.986431.[35] D. C. Rose and J. Chilvers, “Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming,” Front. Sustain. Food Syst., vol. 2, no. December, pp. 1–7, 2018, doi: 10.3389/fsufs.2018.00087.[36] S. O. Araújo, R. S. Peres, J. Barata, F. Lidon, and J. C. Ramalho, “Characterising the agriculture 4.0 landscape— emerging trends, challenges and opportunities,” Agronomy, vol. 11, no. 4, pp. 1–37, 2021, doi: 10.3390/agronomy11040667.[37] A. Monteiro, S. Santos, and P. Gonçalves, “Precision agriculture for crop and livestock farming—Brief review,” Animals, vol. 11, no. 8, pp. 1–18, 2021, doi: 10.3390/ani11082345.[38] V. J. P. D. Martinho and R. de P. F. Guiné, “Integrated-smart agriculture: Contexts and assumptions for a broader concept,” Agronomy, vol. 11, no. 8, p. 1568, 2021, doi: 10.3390/agronomy11081568.[39] I. Cisternas, I. Velásquez, A. Caro, and A. Rodríguez, “Systematic literature review of implementations of precision agriculture,” Comput. Electron. Agric., vol. 176, p. 105626, 2020, doi: 10.1016/j.compag.2020.105626.[40] S. A. Kumar and P. Ilango, “The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review,” Wirel. Pers. Commun., vol. 98, pp. 685–698, 2018, doi: https://doi.org/10.1007/s11277-017-4890-z.[41] W. Dargie and C. Poellabauer, Fundamentals of Wireless Sensor Networks, Primera. Wiley, 2010.[42] T. Ojha, S. Misra, and N. S. Raghuwanshi, “Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges,” Comput. Electron. Agric., vol. 118, pp. 66–84, 2015, doi: 10.1016/j.compag.2015.08.011.[43] K. Sohraby, D. Minoli, and T. Znati, Wireless Sensor Networks. 2007.[44] D. Thakur, Y. Kumar, A. Kumar, and P. K. Singh, Applicability of Wireless Sensor Networks in Precision Agriculture: A Review. Springer US, 2019.[45] R. P. Sharma, D. Ramesh, P. Pal, S. Tripathi, and C. Kumar, “Crop Pest Prediction,” IEEE Internet Things J., vol. 9, no. 4, pp. 3037–3045, 2022, doi: 10.1109/JIOT.2021.3094198.[46] A. K. M. Al-Qurabat, “A Lightweight Huffman-based Differential Encoding Lossless Compression Technique in IoT for Smart Agriculture,” Int. J. Comput. Digit. Syst., vol. 11, no. 1, pp. 117–127, 2022, doi: 10.12785/ijcds/110109.[47] S. Khairunnniza-Bejo, N. H. Ramli, and F. M. Muharam, “Wireless sensor network (WSN) applications in plantation canopy areas: A review,” Asian J. Sci. Res., vol. 11, no. 2, pp. 151–161, 2018, doi: 10.3923/ajsr.2018.151.161.[48] M. Srbinovska, C. Gavrovski, V. Dimcev, A. Krkoleva, and V. Borozan, “Environmental parameters monitoring in precision agriculture using wireless sensor networks,” J. Clean. Prod., vol. 88, pp. 297–307, 2015, doi: 10.1016/j.jclepro.2014.04.036.[49] N. Sabri, S. S. Mohammed, S. Fouad, A. A. Syed, F. T. Al-Dhief, and A. Raheemah, “Investigation of Empirical Wave Propagation Models in Precision Agriculture,” in MATEC Web of Conferences, 2018, vol. 150, p. 06020, doi: https://doi.org/10.1051/matecconf/201815006020.[50] N. Sabri, S. A. Aljunid, M. S. Salim, and S. Fouad, “Wireless Sensor Network Wave Propagation in Vegetation,” Recent Trends Phys. Mater. Sci. Technol. Springer Ser. Mater. Sci., vol. 204, pp. 283–298, 2015, doi: https://doi.org/10.1007/978-981-287-128-2_18.[51] ITU-R, “ITU-R Recommendation P.833-7 Attenuation in vegetation,” 2012.[52] D. L. Ndzi et al., “Vegetation attenuation measurements and modeling in plantations for wireless sensor network planning,” Prog. Electromagn. Res. B, vol. 36, pp. 283–301, 2011, doi: 10.2528/PIERB11091908.[53] T. O. Olasupo, A. Alsayyari, C. E. Otero, K. O. Olasupo, and I. Kostanic, “Empirical path loss models for low power wireless sensor nodes deployed on the ground in different terrains,” in 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2017, pp. 1–8, doi: 10.1109/AEECT.2017.8257747.[54] T. S. Rappaport, Wireless Communications: Principles and practice, Segunda ed. New Jersey: Prentice Hall, 2002.[55] C. Phillips, D. Sicker, and D. Grunwald, “A survey of wireless path loss prediction and coverage mapping methods,” IEEE Commun. Surv. Tutorials, vol. 15, no. 1, pp. 255–270, 2013, doi: 10.1109/SURV.2012.022412.00172.[56] H. S. Jo, C. Park, E. Lee, H. K. Choi, and J. Park, “Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network and gaussian process,” Sensors (Switzerland), vol. 20, no. 7, 2020, doi: 10.3390/s20071927.[57] A. Burkov, The Hundred-Page Machine Learning Book. 2019.[58] K. Murphy, Machine learning : a probabilistic perspective. Cambridge, Massachusetts: The MIT Press, 2012.[59] G. S. Kori and M. S. Kakkasageri, “Classification And Regression Tree (CART) based resource allocation scheme for Wireless Sensor Networks,” Comput. Commun., vol. 197, no. June 2022, pp. 242–254, 2023, doi: 10.1016/j.comcom.2022.11.003.[60] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, pp. 7–8, 2019, doi: 10.3390/app9091908.[61] A. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput., vol. 14, no. 1, pp. 199– 222, 2004, doi: 10.1186/s12984-021-00806-6.[62] A. Ghatak, Machine Learning with R. Singapur: Springer, 2017.[63] O. Theobald, Machine Learning For Absolute Beginners, Segunda. Scatterplot Press, 2017.[64] H. Kumar Gianey and R. Choudhary, “Comprehensive Review On Supervised Machine Learning Algorithms,” in 2017 International Conference on Machine learning and Data Science, 2017, pp. 38–43, doi: 10.1109/MLDS.2017.11.[65] G. Chitralekha and J. M. Roogi, “A Quick Review of ML Algorithms,” in Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021, 2021, pp. 1–5, doi: 10.1109/ICCES51350.2021.9488982.[66] P. Probst, M. N. Wright, and A. L. Boulesteix, “Hyperparameters and tuning strategies for random forest,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 9, no. 3, pp. 1–15, 2019, doi: 10.1002/widm.1301.[67] L. Breiman, “Random Forest,” Mach Learn, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1007/978-3-030-62008- 0_35.[68] N. Moraitis, L. Tsipi, D. Vouyioukas, A. Gkioni, and S. Louvros, “Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz,” Wirel. Networks, vol. 27, no. 6, pp. 4169– 4188, 2021, doi: 10.1007/s11276-021-02682-3.[69] A.-L. Boulesteix, S. Janitza, J. Kruppa, and I. R. Konig, “Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics,” WIREs Data Min. Knowl. Discov., vol. 2, no. 6, pp. 493–507, 2012, doi: https://doi.org/10.1002/widm.1072.[70] A. Ziegler and I. R. König, “Mining data with random forests: current options for real-world applications,” WIREs Data Min. Knowl. Discov., vol. 4, no. 1, pp. 55–63, 2013, doi: https://doi.org/10.1002/widm.1114.[71] M. Kuhn and K. Johnson, Applied Predictive Modeling. New York: Springer, 2013.[72] S. Muthukumaran, P. Geetha, and E. Ramaraj, “Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model,” Intell. Autom. Soft Comput., vol. 35, no. 1, pp. 215–230, 2023, doi: https://doi.org/10.32604/iasc.2023.027449.[73] N. N. Mrabti et al., “Molecular Docking and QSAR Studies for Modeling the Inhibitory Activity of Pyrazolebenzimidazolone Hybrids as Novel Inhibitors of Human 4-hydroxyphenylpyruvate dioxygenase Against Type I Tyrosinemia Disease,” Biointerface Res. Appl. Chem., vol. 13, no. 1, pp. 1–17, 2023, doi: 8 https://doi.org/10.33263/BRIAC131.038.[74] G. Sarker, “A Survey on Convolution Neural Networks,” in 2020 IEEE Region 10 Conference (TENCON), 2020, pp. 923–928, doi: 10.1109/TENCON50793.2020.9293902.[75] Minciencias, “Cuadro tipología de productos,” 2020. .[76] S. Kurt and B. Tavli, “Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations,” Ieee Antennas Propag. Mag., vol. 59, no. 1, pp. 18–37, 2017, doi: 10.1109/MAP.2016.2630035.[77] L. M. Kamarudin, R. B. Ahmad, B. L. Ong, F. Malek, A. Zakaria, and M. A. M. Arif, “Review and modeling of vegetation propagation model for wireless sensor networks using OMNeT++,” in Proceedings - 2nd International Conference on Network Applications, Protocols and Services, NETAPPS 2010, 2010, pp. 78–83, doi: 10.1109/NETAPPS.2010.21.[78] A. Kochhar and N. Kumar, “Wireless sensor networks for greenhouses: An end-to-end review,” Comput. Electron. Agric., vol. 163, no. July, 2019, doi: 10.1016/j.compag.2019.104877.[79] R. Anzum et al., “A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145397.[80] M. Sander-Frigau et al., “A Measurement Study of TVWS Wireless Channels in Crop Farms,” Proc. - 2021 IEEE 18th Int. Conf. Mob. Ad Hoc Smart Syst. MASS 2021, pp. 344–354, 2021, doi: 10.1109/MASS52906.2021.00051.[81] D. Cama-Pinto, J. A. Holgado-Terriza, M. Damas-Hermoso, F. Gómez-Mula, and A. Cama-Pinto, “RadioWave Attenuation Measurement System Based on RSSI for Precision Agriculture: Application to Tomato Greenhouses,” Inventions, vol. 6, no. 4, p. 66, 2021, doi: https://doi.org/10.3390/inventions6040066.[82] L. Juan-Llacer, D. P. Riquelme, J. M. Molina-Garcia-Pardo, J. V. Rodriguez, M. T. Martinez-Ingles, and J. Pascual-Garcia, “A Simplified Model for Path Loss Estimation in Citrus Plantations at 3.5 GHz,” IEEE Antennas Wirel. Propag. Lett., vol. 21, no. 6, pp. 1183–1187, 2022, doi: 10.1109/LAWP.2022.3161098.[83] D. Cama-Pinto et al., “Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions,” Sensors, vol. 20, no. 22, pp. 1–18, 2020, doi: 10.3390/s20226621.[84] P. Avila-Campos, F. Astudillo-Salinas, and A. Vazquez-Rodas, “Evaluation of LoRaWAN Transmission Range for Wireless Sensor Networks in Riparian Forests,” in MSWiM 2019 - Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2019, pp. 199–206, doi: https://doi.org/10.1145/3345768.3355934.[85] O. Artemenko, A. Rubina, A. H. Nayak, S. Baptist, and A. Mitschele-thiel, “Evaluation of di ff erent signal propagation models for a mixed indoor-outdoor scenario using empirical data,” EAI Endorsed Trans. Mob. Commun. Appl., vol. 2, no. 7, pp. 1–8, 2016, doi: 10.4108/eai.20-6-2016.151519.[86] N. Devarajan and S. H. Gupta, “Implementation and Analysis of Different Path Loss Models for Cooperative Communication,” Smart Innov. Commun. Comput. Sci. Adv. Intell. Syst. Comput., vol. 851, 2019, doi: https://doi.org/10.1007/978-981-13-2414-7_22.[87] C.-X. Wang, J. Bian, J. Sun, W. Zhang, and M. Zhang, “A Survey of 5G Channel Measurements and Models,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 3142–3168, 2018, doi: 10.1109/COMST.2018.2862141.[88] S. Duangsuwan, P. Juengkittikul, and M. Myint Maw, “Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios,” Int. J. Antennas Propag., vol. 2021, 2021, doi: 10.1155/2021/5524709.[89] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091908.[90] G. Vergos, S. P. Sotiroudis, G. Athanasiadou, G. V. Tsoulos, and S. K. Goudos, “Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction,” in 2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021, 2021, pp. 1–4, doi: 10.1109/MOCAST52088.2021.9493374.[91] C. A. Oroza, Z. Zhang, T. Watteyne, and S. D. Glaser, “A Machine-Learning-Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, pp. 576–584, 2017, doi: 10.1109/TCCN.2017.2741468.[92] S. K. Goudos, G. Athanasiadou, G. V. Tsoulos, and V. Rekkas, “Modelling Ray Tracing Propagation Data Using Different Machine Learning Algorithms,” 14th Eur. Conf. Antennas Propagation, EuCAP 2020, 2020, doi: 10.23919/EuCAP48036.2020.9135639.[93] M. K. Elmezughi, O. Salih, T. J. Afullo, and K. J. Duffy, “Comparative Analysis of Major Machine-LearningBased Path Loss Models for Enclosed Indoor Channels,” Sensors, vol. 22, no. 13, pp. 1–25, 2022, doi: 10.3390/s22134967.[94] A. L. Barcellos, J. C. Duarte, and A. C. Mendes, “Radiofrequency Signal Levels Predition Using Machine Learning Models,” IEEE Lat. Am. Trans., vol. 21, no. 2, pp. 351–357, 2023, doi: 10.1109/TLA.2023.10015229.[95] O. J. Famoriji and T. Shongwe, “Path Loss Prediction in Tropical Regions using Machine Learning Techniques: A Case Study,” Electron., vol. 11, no. 17, pp. 1–13, 2022, doi: 10.3390/electronics11172711.[96] N. Kuno, W. Yamada, M. Inomata, M. Sasaki, Y. Asai, and Y. Takatori, “Evaluation of characteristics for NN and CNN in path loss prediction,” in 2020 International Symposium on Antennas and Propagation, ISAP 2020, 2021, pp. 61–62, doi: 10.23919/ISAP47053.2021.9391493.[97] R. O. Abolade, S. O. Famakinde, S. I. Popoola, O. F. Oseni, A. A. Atayero, and S. Misra, “Support Vector Machine for Path Loss Predictions in Urban Environment,” in ICCSA 2020: Computational Science and Its Applications. Lecture Notes in Computer Science, 2020, vol. 12255, pp. 995–1006, doi: 10.1007/978-3-030- 58820-5_71.[98] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure,” IEEE Trans. Ind. Informatics, vol. 18, no. 10, pp. 6981–6990, 2022, doi: 10.1109/TII.2022.3142318.[99] C. E. García and I. Koo, “Extremely Randomized Trees Regressor Scheme for Mobile Network Coverage Prediction and REM Construction,” IEEE Access, vol. 11, no. July, pp. 65170–65180, 2023, doi: 10.1109/ACCESS.2023.3287103.[100] K. Kayaalp, S. Metlek, A. Genc, H. Dogan, and İ. B. Basyigit, “Prediction of path loss in coastal and vegetative environments with deep learning at 5G sub-6 GHz,” Wirel. Networks, vol. 0123456789, pp. 2471–2480, 2023, doi: 10.1007/s11276-023-03285-w.[101] M. González-Palacio, D. Tobón-Vallejo, L. Sepúlveda-Cano, S. Rúa, and L. Bao Le, “Machine-Learning-Based Combined Path Loss and Shadowing Model in LoRaWAN for Energy Efficiency Enhancement,” EEE Internet Things J., vol. 10, no. 12, pp. 10725–10739, 2023, doi: 10.1109/JIOT.2023.3239827.[102] R. T. Juang, “Path loss modelling based on path profile in urban propagation environments,” IET Commun., vol. 16, no. 6, pp. 685–694, 2022, doi: 10.1049/cmu2.12369.[103] K. J. Jang et al., “Path Loss Model Based on Machine Learning Using Multi-Dimensional Gaussian Process Regression,” IEEE Access, vol. 10, no. November, pp. 115061–115073, 2022, doi: 10.1109/ACCESS.2022.3217912.[104] H. M. Jawad et al., “Accurate Empirical Path-Loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture,” IEEE Sens. J., vol. 20, no. 1, pp. 552–561, 2020, doi: 10.1109/JSEN.2019.2940186.[105] H. Wu, Y. Miao, F. Li, and L. Zhu, “Empirical modeling and evaluation of multi-path radio channels on wheat farmland based on communication quality,” Trans. ASABE, vol. 59, no. 3, pp. 759–767, 2016, doi: 10.13031/trans.59.11016.[106] D. Cama-Pinto, M. Damas, J. A. Holgado-Terriza, F. Gómez-Mula, and A. Cama-Pinto, “Path loss determination using linear and cubic regression inside a classic tomato greenhouse,” Int. J. Environ. Res. Public Health, vol. 16, no. 10, p. 1744, 2019, doi: https://doi.org/10.3390/ijerph16101744.[107] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “NSGA-III Based Heterogeneous Transmission Range Selection for Node Deployment in IEEE 802.15.4 Infrastructure for Sugarcane and Rice Crop Monitoring in a Humid Sub-Tropical Region,” IEEE Trans. Wirel. Commun., vol. 22, no. 6, pp. 3643–3656, 2023, doi: 10.1109/TWC.2022.3220146.[108] S. Phaiboon and P. Phokharatkul, “Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression,” Sensors, vol. 23, no. 7, pp. 1–20, 2023, doi: 10.3390/s23073525.[109] N. Leonor, S. Faria, M. Vala, and R. F. S. Caldeirinha, “A Combined ITM and LITU-R Model for Enhanced Radio Coverage Predictions of Mission-Critical Communications in Mountainous Vegetated Terrains,” IEEE Antennas Wirel. Propag. Lett., vol. 21, no. 9, pp. 1777–1781, 2022, doi: 10.1109/LAWP.2022.3179625.[110] G. Hakim et al., “Near Ground Pathloss Propagation Model Using Adaptive Communication in Forest , Jungle and Open Dirt,” Sensors, vol. 22, no. 3267, 2022, doi: 10.3390/s22093267.[111] Y. Voutos, P. Mylonas, E. Spyrou, and E. Charou, “An IoT-based insular monitoring architecture for smart viticulture,” 2019, doi: 10.1109/IISA.2018.8633630.[112] N. Karimi, A. Arabhosseini, M. Karimi, and M. H. Kianmehr, “Web-based monitoring system using Wireless Sensor Networks for traditional vineyards and grape drying buildings,” Comput. Electron. Agric., vol. 144, no. December 2016, pp. 269–283, 2018, doi: 10.1016/j.compag.2017.12.018.[113] F. Muzafarov and A. Eshmuradov, “Wireless sensor network based monitoring system for precision agriculture in Uzbekistan,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 3, pp. 1071–1080, 2019, doi: 10.12928/TELKOMNIKA.V17I3.11513.[114] S. Phaiboon and P. Phokharatkul, “An Empirical Model For 433 MHz LoRa-WAN in Ruby Mango Plantation,” in 2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST), 2023, no. 3, pp. 25–28, doi: 10.1109/iceast58324.2023.10157266.[115] T. H. Nasution, M. Yasir, Fahmi, and Soeharwinto, “Designing an IoT system for monitoring and controlling temperature and humidity in mushroom cultivation fields,” in ICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding, 2019, pp. 326–331, doi: 10.1109/ICECOS47637.2019.8984446.[116] N. Kaur and G. Deep, “IoT-Based Brinjal Crop Monitoring System,” in Smart Sensors for Industrial Internet of Things, 2021.[117] E. P. Kho, S. N. D. Chua, S. F. Lim, L. C. Lau, and M. T. N. Gani, “Development of young sago palm environmental monitoring system with wireless sensor networks,” Comput. Electron. Agric., vol. 193, no. September 2021, p. 106723, 2022, doi: 10.1016/j.compag.2022.106723.[118] L. Yu et al., “Assessment of cornfield LAI retrieved from multi-source satellite data using continuous field LAI measurements based on a wireless sensor network,” Remote Sens., vol. 12, no. 20, pp. 1–19, 2020, doi: 10.3390/rs12203304.[119] A. Zabasta, A. Avotins, R. Porins, P. Apse-Apsitis, J. Bicans, and D. Korabicka, “Development of IoT based Monitoring and Control System for Small Industrial Greenhouses,” in 2021 10th Mediterranean Conference on Embedded Computing, MECO 2021, 2021, pp. 7–10, doi: 10.1109/MECO52532.2021.9460230.[120] A. Touhami, K. Benahmed, and F. Bounaama, “Monitoring of Greenhouse Based on Internet of Things and Wireless Sensor Network,” 2020, doi: https://doi-org.ezproxy.cuc.edu.co/10.1007/978-3-030-21009-0_27.[121] D. P. Rubanga, K. Hatanaka, and S. Shimada, “Development of a simplified smart agriculture system for smallscale greenhouse farming,” Sensors Mater., vol. 31, no. 3, pp. 831–843, 2019, doi: 10.18494/SAM.2019.2154.[122] K. P. Ferentinos, N. Katsoulas, A. Tzounis, T. Bartzanas, and C. Kittas, “Wireless sensor networks for greenhouse climate and plant condition assessment,” Biosyst. Eng., vol. 153, pp. 70–81, 2017, doi: 10.1016/j.biosystemseng.2016.11.005.[123] S. Kaur and Deepali, “An automatic irrigation system for different crops with WSN,” in 2017 6th International Conference on Reliability, Infocom Technologies and Optimization: Trends and Future Directions, ICRITO 2017, 2018, vol. 2018-Janua, pp. 406–411, doi: 10.1109/ICRITO.2017.8342460.[124] Y. Mohanraj, V. . Gokul, R. Ezhilarasie, and U. Umamakeswari, “Intelligent Drip Irrigation and Fertigation Using,” in 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2017, pp. 36–41, doi: 10.1109/TIAR.2017.8273682.[125] R. Khan, I. Ali, M. Zakarya, M. Ahmad, M. Imran, and M. Shoaib, “Technology-Assisted Decision Support System for Efficient Water Utilization: A Real-Time Testbed for Irrigation Using Wireless Sensor Networks,” IEEE Access, vol. 6, pp. 25686–25697, 2018, doi: 10.1109/ACCESS.2018.2836185.[126] N. Penchalaiah, J. Nelson Emmanuel, S. Suraj Kamal, and K. Ramana, “IoT Based Automatic Irrigation System Using Wireless Sensor Networks,” in Lecture Notes in Electrical Engineering, 2021.[127] I. Picallo et al., “A radio channel model for D2D communications blocked by single trees in forest environments,” Sensors, vol. 19, no. 21, p. 4606, 2019, doi: https://doi.org/10.3390/s19214606.[128] F. Correia Pinheiro, M. Sampaio De Alencar, W. T. Araújo Lopes, M. Soares De Assis, and B. Gonçalves Leal, “Propagation analysis for wireless sensor networks applied to viticulture,” Int. J. Antennas Propag., vol. 2017, 2017, doi: https://doi.org/10.1155/2017/7903839 Research.[129] Q. M. Qadir, T. A. Rashid, N. K. Al-Salihi, B. Ismael, A. A. Kist, and Z. Zhang, “Low power wide area networks: A survey of enabling technologies, applications and interoperability needs,” IEEE Access, vol. 6, pp. 77454–77473, 2018, doi: 10.1109/ACCESS.2018.2883151.[130] Lora Alliance, “What is LoRaWAN Specification,” What is LoRaWAN, 2021. https://lora-alliance.org/aboutlorawan/ (accessed Feb. 01, 2021).[131] J. De Carvalho Silva, J. J. P. C. Rodrigues, A. M. Alberti, P. Solic, and A. L. L. Aquino, “LoRaWAN - A low power WAN protocol for Internet of Things: A review and opportunities,” in 2017 2nd International Multidisciplinary Conference on Computer and Energy Science, SpliTech 2017, 2017, pp. 1–6.[132] A. Lombardo, S. Parrino, G. Peruzzi, and A. Pozzebon, “LoRaWAN Versus NB-IoT: Transmission Performance Analysis Within Critical Environments,” IEEE Internet Things J., vol. 9, no. 2, pp. 1068–1081, 2022, doi: 10.1109/JIOT.2021.3079567.[133] H. Sharma and S. Sharma, “A review of sensor networks: Technologies and applications,” in 2014 Recent Advances in Engineering and Computational Sciences, RAECS 2014, 2014, pp. 6–8, doi: 10.1109/RAECS.2014.6799579.[134] S. Al-sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, “Review,” pp. 685–690, 2017.[135] W. Tang, X. Ma, J. Wei, and Z. Wang, “Measurement and analysis of near-ground propagation models under different terrains for wireless sensor networks,” Sensors, vol. 19, no. 8, p. 1901, 2019, doi: https://doi.org/10.3390/s19081901.[136] H. Wu, L. Zhang, and Y. Miao, “The Propagation Characteristics of Radio Frequency Signals for Wireless Sensor Networks in Large-Scale Farmland,” Wirel. Pers. Commun., vol. 95, no. 4, pp. 3653–3670, 2017, doi: 10.1007/s11277-017-4018-5.[137] T. Rama Rao, D. Balachander, and N. Tiwari, “RF Propagation Measurements in Forest & Plantation Environments for Wireless Sensor Networks,” in 2012 IEEE International Conference on Communication Systems, ICCS 2012, 2012, pp. 194–198, doi: 10.1109/ICCS.2012.6406137[138] H. T. Anastassiu et al., “A computational model for path loss in wireless sensor networks in orchard environments,” Sensors, vol. 14, no. 3, pp. 5118–5135, 2014, doi: https://doi.org/10.3390/s140305118.[139] D. L. Ndzi et al., “Wireless sensor network coverage measurement and planning in mixed crop farming,” Comput. Electron. Agric., vol. 105, pp. 83–94, 2014, doi: 10.1016/j.compag.2014.04.012.[140] J. A. Azevedo and F. E. Santos, “A model to estimate the path loss in areas with foliage of trees,” AEU - Int. J. Electron. Commun., vol. 71, pp. 157–161, 2017, doi: 10.1016/j.aeue.2016.10.018.[141] X. ming Guo, X. ting Yang, M. xiang Chen, M. Li, and Y. an Wang, “A model with leaf area index and apple size parameters for 2.4 GHz radio propagation in apple orchards,” Precis. Agric., vol. 16, no. 2, pp. 180–200, 2015, doi: 10.1007/s11119-014-9369-2.[142] T. O. Olasupo and C. E. Otero, “The Impacts of Node Orientation on Radio Propagation Models for AirborneDeployed Sensor Networks in Large-Scale Tree Vegetation Terrains,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 50, no. 1, pp. 256–269, 2020, doi: 10.1109/TSMC.2017.2737473.[143] Z. Gao et al., “Wireless channel propagation characteristics and modeling research in rice field sensor networks,” Sensors, vol. 18, no. 9, p. 3116, 2018, doi: https://doi.org/10.3390/s18093116.[144] A. Raheemah, N. Sabri, M. S. Salim, P. Ehkan, and R. B. Ahmad, “New empirical path loss model for wireless sensor networks in mango greenhouses,” Comput. Electron. Agric., vol. 127, pp. 553–560, 2016, doi: 10.1016/j.compag.2016.07.011.[145] G. P. N. Hakim, M. Alaydrus, and R. B. Bahaweres, “Empirical approach of ad hoc path loss propagation model in realistic forest environments,” Proceeding - 2016 Int. Conf. Radar, Antenna, Microwave, Electron. Telecommun. ICRAMET 2016, pp. 139–143, 2017, doi: 10.1109/ICRAMET.2016.7849600.[146] N. Shutimarrungson and P. Wuttidittachotti, “Realistic propagation effects on wireless sensor networks for landslide management,” EURASIP J. Wirel. Commun. Netw., vol. 94, 2019, doi: https://doi.org/10.1186/s13638-019-1412-6.[147] J. A. Gay-Fernández and I. Cuiñas, “Peer to peer wireless propagation measurements and path-loss modeling in vegetated environments,” IEEE Trans. Antennas Propag., vol. 61, no. 6, pp. 3302–3311, 2013, doi: 10.1109/TAP.2013.2254452.[148] Y. Rao, Z. hui Jiang, and N. Lazarovitch, “Investigating signal propagation and strength distribution characteristics of wireless sensor networks in date palm orchards,” Comput. Electron. Agric., vol. 124, pp. 107– 120, 2016, doi: 10.1016/j.compag.2016.03.023.[149] A. AlSayyari, I. Kostanic, and C. E. Otero, “An Empirical Path Loss Model for Wireless Sensor Network Deployment in a Dense Tree Environment,” in 2017 IEEE Sensors Applications Symposium (SAS), 2017, pp. 1–6, doi: 10.1109/SAS.2017.7894099.[150] A. Navarro, D. Guevara, and G. A. Florez, “An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields,” in 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020, no. September, pp. 1–3, doi: 10.23919/URSIGASS49373.2020.9232365.[151] T. Hamasaki, “Propagation Characteristics of A 2.4GHz Wireless Sensor Module with A Pattern Antenna in Forestry and Agriculture Field,” in 2019 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), 2019, pp. 1–3, doi: 10.1109/RFIT.2019.8929207.[152] A. Alsayyari and A. Aldosary, “Path loss results for wireless sensor network deployment in a sparse tree environment,” 2019 Int. Symp. Networks, Comput. Commun., vol. 1–6, pp. 1–6, 2019, doi: 10.1109/ISNCC.2019.8909137.[153] X. Xu et al., “Measurement and Analysis of Wireless propagative Model of 433MHz and 2.4GHz Frequency in Southern China Orchards,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 695–699, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.08.115.[154] DIGI, “Digi XBee 3 Zigbee Mesh Kit, worldwide,” 2023. https://www.digi.com/products/models/xk3-z8s-wzm (accessed Oct. 21, 2023).[155] A. Barrios-Ulloa, P. P. Ariza-Colpas, H. Sánchez-Moreno, A. P. Quintero-Linero, and E. De la Hoz-Franco, “Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145285.[156] Agencia Nacional del Espectro, “Espectro para atender el crecimiento futuro y la masificación de aplicaciones IoT,” 2022. https://www.ane.gov.co/Sliders/archivos/gestión técnica/Estudios de gestión y planeación/Espectro para IoT/Documentos para consulta/DocumentoEspectroIoT.pdf (accessed Oct. 21, 2023).[157] Agencia Nacional del Espectro, “DOCUMENTO DE CONSULTA PÚBLICA SOBRE LAS BANDAS DE FRECUENCIAS DISPONIBLES PARA EL FUTURO DESARROLLO DE LAS TELECOMUNICACIONES MÓVILES INTERNACIONALES (IMT) EN COLOMBIA,” 2020. https://www.ane.gov.co/Documentos compartidos/ArchivosDescargables/noticias/Consulta pública sobre las bandas disponibles para el futuro desarrollo de las IMT en Colombia.pdf (accessed Oct. 21, 2023).[158] Agencia Nacional del Espectro, “Consulta pública para la banda de 900 MHz,” 2021. https://www.ane.gov.co/Sliders/archivos/gestión técnica/Estudios de gestión y planeación/900 MHz/Documentos/Consulta publica banda de 900 MHz .pdf (accessed Oct. 21, 2023).[159] M. Aguilera Díaz, “Documentos de Trabajo Sobre Economía Regional,” 2012. [Online]. Available: https://www.banrep.gov.co/sites/default/files/publicaciones/archivos/dtser_158.pdf.[160] D. Álvarez Arroyo, “Entrevista técnica privada,” 2022.[161] H. Dogan, “A new empirical propagation model depending on volumetric density in citrus orchards for wireless sensornetwork applications at sub-6 GHz frequency region,” Int. J. RF Microw. Comput. Eng., vol. 31, no. 9, p. e22778, 2021, doi: https://doi.org/10.1002/mmce.22778.[162] P. Phokharatkul and S. Phaiboon, “Path Loss Model for the Bananas and Weeds Environment Based on Grey System Theory,” in 2021 Photonics & Electromagnetics Research Symposium (PIERS), 2021, pp. 413–418, doi: 10.1109/PIERS53385.2021.9694777.[163] K. Kayaalp, S. Metlek, A. Genc, H. Dogan, and B. B. Bahadir Basyigitİbrahim, “Prediction of path loss in coastal and vegetative environments with deep learning at 5G sub-6 GHz,” Wirel. Networks, vol. 29, pp. 2471– 2480, 2023, doi: https://doi.org/10.1007/s11276-023-03285-w.[164] D. Cama-Pinto, “Modelos Empíricos de Radio Propagación en Presencia de Vegetación para Aplicaciones Agrícolas,” Universidad de Granada, 2022.[165] L. Juan-Llacer et al., “Path Loss Measurements and Modelling in a Citrus Plantation in the 1800 MHz, 3.5 GHz and 28 GHz in LoS,” 2022 16th Eur. Conf. Antennas Propagation, EuCAP 2022, no. 1, 2022, doi: 10.23919/eucap53622.2022.9769016.[166] H. Pan, Y. Shi, X. Wang, and T. Li, “Modeling wireless sensor networks radio frequency signal loss in corn environment,” Multimed. Tools Appl., vol. 76, no. 19, pp. 19479–19490, 2017, doi: https://doi.org/10.1007/s11042-015-3150-z.[167] S. Khalid, T. Khalil, and S. Nasreen, “A survey of feature selection and feature extraction techniques in machine learning,” in Proceedings of 2014 Science and Information Conference, SAI 2014, 2014, no. October, pp. 372– 378, doi: 10.1109/SAI.2014.6918213.[168] A. Barrios-Ulloa, A. Cama-Pinto, E. De-la-Hoz-Franco, R. Velarde-Ramírez, and D. Cama-Pinto, “Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning,” Agric., vol. 13, no. 11, p. 2046, 2023, doi: 10.3390/agriculture13112046.[169] O. Theoblad, Machine Learning For Absolute Beginners, Segunda. Scatterplot Press, 2014.[170] E. Alpaydın, Introduction to Machine Learning, Tercera. Cambridge, Massachusetts: The MIT Press, 2014.[171] ITU-R, “Recommendation ITU-R P.1411-12,” 2023. https://www.itu.int/dms_pubrec/itu-r/rec/p/R-RECP.1411-12-202308-I!!PDF-E.pdf (accessed Mar. 26, 2023).[172] J. Zhang, L. Liu, Y. Fan, L. Zhuang, T. Zhou, and Z. Piao, “Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning,” IEEE Access, vol. 8, pp. 47797–47806, 2020, doi: 10.1109/ACCESS.2020.2979220.[173] A. Kochhar, N. Kumar, and U. Arora, “Signal Assessment Using ML for Evaluation of WSN Framework in greenhouse monitoring,” Int. J. Sensors , Wirel. Commun. Control, vol. 12, no. 09, pp. 669–679, 2022, doi: 0.2174/2210327913666221220154338.[174] T. Nagao and T. Hayashi, “Fine-Tuning for Propagation Modeling of Different Frequencies with Few Data,” IEEE Veh. Technol. Conf., vol. 2022-Septe, pp. 1–5, 2022, doi: 10.1109/VTC2022-Fall57202.2022.10012911.[175] S. Wu, B. Ma, T. Ye, J. Zhang, W. Shao, and W. Zheng, “A Machine Learning based Intelligent Propagation Model for RSRP prediction,” in Proceedings - 2022 International Seminar on Computer Science and Engineering Technology, SCSET 2022, 2022, pp. 1–5, doi: 10.1109/SCSET55041.2022.00010.[176] S. K. Goudos, G. Athanasiadou, G. V. Tsoulos, and V. Rekkas, “Modelling Ray Tracing Propagation Data Using Different Machine Learning Algorithms,” 2020, doi: 10.23919/EuCAP48036.2020.9135639.[177] S. Ojo, A. Sari, and T. P. Ojo, “Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models,” Open J. Appl. Sci., vol. 12, no. 06, pp. 990–1010, 2022, doi: 10.4236/ojapps.2022.126068.Cultivos de yucaModelos de pérdida de rutaPropagación de ondas de radioRedes inalámbricas de sensoresMachine learningCassava bushesPath los modelsModelling wave propagationWireless sensor networksPublicationORIGINALModelamiento de la propagación de ondas de radio.pdfModelamiento de la propagación de ondas de radio.pdfTesisapplication/pdf3062167https://repositorio.cuc.edu.co/bitstreams/bc5c2d15-e1b2-4407-b4d4-2ac46aa120ad/download8d31ced6d5d458b9de867dc49b10fd6bMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/d9b44105-a8f4-41c6-bdde-59545a85ddfe/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTModelamiento de la propagación de ondas de radio.pdf.txtModelamiento de la propagación de ondas de radio.pdf.txtExtracted texttext/plain208920https://repositorio.cuc.edu.co/bitstreams/69a40a17-3bec-4b9a-bf2b-46973d9d7190/downloadf1b29901ab48de044a16cf7e7ab2b22aMD53THUMBNAILModelamiento de la propagación de ondas de radio.pdf.jpgModelamiento de la propagación de ondas de radio.pdf.jpgGenerated Thumbnailimage/jpeg8668https://repositorio.cuc.edu.co/bitstreams/a1561b10-c2c1-43a7-8632-e5e06d846d99/downloadac38601ac0e90a5dd6c0569322d8b0f7MD5411323/13114oai:repositorio.cuc.edu.co:11323/131142024-09-17 12:50:38.04https://creativecommons.org/licenses/by-nc-sa/4.0/open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Modelamiento de la propagación de ondas de radio para redes inalámbricas de sensores en cultivos de yuca

Publicaciones similares