State of the Art of Agrotechnological Tools in the Rice Farming System

Authors

DOI:

https://doi.org/10.61799/2216-0388.1664

Keywords:

Agrotechnology , Agricultural Automation, Soil management, Sustainability

Abstract

The article explores the use of agrotechnological tools for sustainable soil management in the rice agricultural sector, highlighting the relevance of rice as a staple food for more than one third of the world's population, with one of the largest rice consumers in Latin America. Through a descriptive review, state-of-the-art information on technologies that optimize rice production and promote sustainable land use practices is compiled. Using the PICOP (Population, Intervention, Comparison, Outcome, and Context) framework to define search terms and select relevant publications since 2016, theses and web publications are excluded, and scientific databases such as, Scopus, Web of Science and Google Scholar are resorted to, applying specific combinations of terms related to soil management and agricultural technology. The results address the need for automation in agriculture to improve production efficiency and plant health. Highlighting advanced technologies and software that enable farmers to make informed decisions about soil management and crop nutrition. The most developed digital solutions include robotics, the Internet of Things (IoT), GPS, GIS and Machine Learning, as well as specialized software that provides nutritional plans and fertilization recommendations based on detailed analysis of the soil and crop health. The most developed digital solutions include robotics, the Internet of Things (IoT), GPS, GIS and Machine Learning, as well as specialized software that provides nutritional plans and fertilization recommendations based on detailed soil analysis. The article emphasizes how the integration of technology in agriculture not only increases productivity and profitability, but also supports environmental sustainability. It concludes by highlighting the importance of continuing to evaluate and optimize these technologies to make them accessible and economically viable for farmers, especially in developing regions, emphasizing the need to continue adapting and improving these tools to maximize their benefits in global agriculture.

Downloads

Download data is not yet available.

References

[1] Statista, "Consumo de arroz en América Latina y el Caribe en 2023, por país," 2023. [En línea]. Disponible en: https://es.statista.com/estadisticas/1479482/consumo-de-arroz-latinoamerica-caribe/.

[2] Ministerio de Agricultura y Desarrollo Rural de Colombia, "Agricultura de Precisión," 2021. [En línea]. Disponible en: https://www.minagricultura.gov.co/servicios/Documents/Agricultura-de-Precision.pdf.

[3] SJA Guirao Goris, “Utilidad y tipos de revisión de literatura”, Ene , vol. 9, núm. 2, págs. 0-0, 2015 DOI: https://doi.org/10.4321/S1988-348X2015000200002

[4] J. Hevia, Á. Huete, S. Alfaro, y V. Palominos, "Herramientas útiles y métodos de búsqueda bibliográfica en PubMed: guía paso a paso para médicos," Revista médica de Chile, vol. 145, no. 12, pp. 1610–1618, 2017. http://dx.doi.org/10.4067/s0034-98872017001201610 DOI: https://doi.org/10.4067/s0034-98872017001201610

[5] D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, y PRISMA Group, "Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement," Annals of Internal Medicine, vol. 151, no. 4, pp. 264–269, 2009. https://doi.org/10.1016/j.ijsu.2010.02.007 DOI: https://doi.org/10.7326/0003-4819-151-4-200908180-00135

[6] C. Gómez, P. Vallejo, and J. Aguilar, “A Systematic Literature Review on Serious Games Methodologies for Training in the Mining Sector,” Information (Switzerland), vol. 16, no. 5. Multidisciplinary Digital Publishing Institute (MDPI), May 01, 2025, doi: 10.3390/info16050389. DOI: https://doi.org/10.3390/info16050389

[7] R. M. Ureta and R. T. Díaz, “La alfabetización visual en la formación de docentes: revisión sistemática según las directrices PRISMA 2020,” Runae, no. 8, 2023.

[8] L. K. Carpio Santos, “El uso de la tecnología en la agricultura,” Pro Sci. Rev. Prod. Ciencias e Investig., vol. 2, no. 14, 2018, doi: 10.29018/issn.2588-1000vol2iss14.2018pp25-32. https://doi.org/10.29018/issn.2588-1000vol2iss14.2018pp25-32 DOI: https://doi.org/10.29018/issn.2588-1000vol2iss14.2018pp25-32

[9] C. López Gálvez, “Uso de drones, un caso de tecnología avanzada en la agricultura,” Adm. y Tecnol. para el diseño., no. 16, 2016. Disponible en: https://hdl.handle.net/11191/9185

[10] A. Pérez, M. Milla, and M. Mesa, “Impacto de las tecnologías de la información y la comunicación en la agricultura,” Cultivos Tropicales, vol. 27, no. 1, pp. 11–17, 2006. Disponible en: https://www.usmp.edu.pe/campus/pdf/revista29/articulo10.pdf

[11] A. Zúñiga Orozco, “Tecnología CRISPR-Cas9: una herramienta aplicable en la agricultura de Costa Rica,” Repert. Científico, vol. 20, no. 2, 2018, doi: 10.22458/rc.v20i2.2396. DOI: https://doi.org/10.22458/rc.v20i2.2396

[12] C. Idaris, C. Mil, A. Fern, C. Fabre, and D. Gonz, “Software evaluación de expertos por el método Delphy para el pronóstico de la investigación agrícola,” Rev. Ciencias Técnicas Agropecu., vol. 22, no. 4, 2013. Disponible en: https://www-redalyc-org.bdbiblioteca.ufps.edu.co/articulo.oa?id=93231386014

[13] M. J. Ala Delgado, “Sistema automatizado para regular el comportamiento de los factores climáticos en el cultivo de hortalizas,” Rev. Ing., vol. 4, no. 10, pp. 233–252, Sep. 2020, doi: https://doi.org/10.33996/revistaingenieria.v4i10.68 DOI: https://doi.org/10.33996/revistaingenieria.v4i10.68

[14] D. Shende, N. Wyawahare, L. Thakare, and R. Agrawal, "Design Process for Adaptive Spraying of Pesticides Based on Mutual Plant Health Detection and Monitoring: A Review," in Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy (ICAIS), 2023. https://doi.org/10.1109/ICAIS56108.2023.10073695 DOI: https://doi.org/10.1109/ICAIS56108.2023.10073695

[15] Pérez, M. Milla, and M. Mesa, "Impacto de las tecnologías de la información y la comunicación en la agricultura," Cultivos Tropicales, vol. 27, no. 1, pp. 11–17, 2006. Disponible en: http://www.redalyc.org/articulo.oa?id=193215885002

[16] C. E. Rodríguez López and Y. L. Vargas Castiblanco, “Importance of automation processes in the Colombian agricultural,” ID EST-Revista Investig. Desarro. Educ. Serv. y Trabájo, vol. 4, no. 2, 2024. Disponible en: https://www.revista.fundes.edu.co/index.php/revista/article/view/264

[17] K. A. Montechiari, “Automatización y robótica en la Industria lechera: creación de ventajas competitivas y mejora de la rentabilidad en Montechiari Agroindustria,” M.S. thesis, 2023. Disponible en: https://repositorio.21.edu.ar/handle/ues21/27175

[18] A. Atehortua Gonzalez, “Tecnología e innovación: una apuesta para desarrollar el agro colombiano,” Rev. Colomb. Investig. Agroindustriales, vol. 5, no. 2, 2018. https://doi.org/10.23850/24220582.1797 DOI: https://doi.org/10.23850/24220582.1797

[19] P. R. A. Quinteros, M. C. Zurita, N. C. Zambrano, and E. L. Manchay, “Automatización de los procesos industriales,” Journal of Business and Entrepreneurial Studies (JBES), vol. 4, no. 2, pp. 123–131, 2020. Disponible en: https://dialnet-unirioja-es.bdbiblioteca.ufps.edu.co/servlet/articulo?codigo=7888290

[20] C. J. Espín Villafuerte, M. G. Quinatoa Sánchez, M. K. Jaramillo Vásquez, and D. R. Ñacato Estrella, “Aplicación de una interfaz web al proceso manufacturero en la industria agrícola de la papa y el tomate,” LATAM Rev. Latinoam. Ciencias Soc. y Humanidades, vol. 4, no. 1, 2023. https://doi.org/10.56712/latam.v4i1.372 DOI: https://doi.org/10.56712/latam.v4i1.372

[21] S. Santos Valle and J. Kienzle, Agricultura 4.0: Robótica agrícola y equipos automatizados para la producción agrícola sostenible, 2021. Disponible en: https://openknowledge.fao.org/handle/20.500.14283/cb2186es

[22] Mercado et al., “RED SIPIA : Red de Sensores Inalámbricos para Investigación Agronómica,” XIII Work. Investig. en Ciencias la Comput., 2011. Disponible en: http://sedici.unlp.edu.ar/handle/10915/19769

[23] M. McLaughlin, D. Pennock, and N. Rodriguez, La contaminación del suelo: una realidad oculta. 2019. Disponible en: https://openknowledge.fao.org/items/30b955f2-9989-4629-9782-b67dfa518841

[24] J. M. Casso-Gaspar, O. A. Acevedo-Sandoval, and S. Martinez-Hernández, “Contaminación del suelo por microplásticos: panorama actual,” Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI, vol. 10, no. 19, pp. 55–60, 2022. https://doi.org/10.29057/icbi.v10i19.9188 DOI: https://doi.org/10.29057/icbi.v10i19.9188

[25] M. O. Merino, A. R. O. Valencia, R. J. C. Andrade, and M. V. M. Conforme, “Las (TIC) una estrategia para prevenir la contaminación ambiental,” Revista Científica Arbitrada Multidisciplinaria PENTACIENCIAS, vol. 5, no. 3, pp. 634–642, 2023. https://doi.org/10.59169/pentaciencias.v5i3.589 DOI: https://doi.org/10.59169/pentaciencias.v5i3.589

[26] F. García and V. Miranda, “Eutrofización, una amenaza para el recurso hídrico.,” Vol. II la Colección Agenda pública para el Desarro. Reg. la metropolización y la sostenibilidad, 2018. Disponible en: https://ru.iiec.unam.mx:80/id/eprint/4269

[27] J. A. Barraza, E. J. Espinoza, A. G. Espinos, and J. Serracin, “Agricultura de precisión con drones para control de enfermedades en la planta de arroz,” Rev. Iniciación Científica, vol. 5, 2019, doi: https://doi.org/10.33412/rev-ric.v5.0.2368 DOI: https://doi.org/10.33412/rev-ric.v5.0.2368

[28] S. Justice and S. Biggs, "The spread of smaller engines and markets in machinery services in rural areas of South Asia," Journal of Rural Studies, vol. 73, pp. 10–20, 2020. https://doi.org/10.1016/j.jrurstud.2019.11.013 DOI: https://doi.org/10.1016/j.jrurstud.2019.11.013

[29] S. Dwivedy, A. K. Mahto, A. K. Sahoo, and C. Pradhan, "Soil Testing using IoT and Machine Learning Classifiers," in Proceedings of the 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2022. https://doi.org/10.1109/ICAC3N56670.2022.10074391 DOI: https://doi.org/10.1109/ICAC3N56670.2022.10074391

[30] L. Kanuru, A. K. Tyagi, S. Aswathy, T. F. Fernandez, N. Sreenath, and S. Mishra, "Prediction of Pesticides and Fertilizers using Machine Learning and Internet of Things," in Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI), 2021. https://doi.org/10.1109/ICCCI50826.2021.9402536 DOI: https://doi.org/10.1109/ICCCI50826.2021.9402536

[31] M. Meeradevi, V. Sanjana, and M. R. Mundada, "Decision Support System to Agronomically Optimize Crop Yield based on Nitrogen and Phosphorus," in Proceedings of the 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), 2019. https://doi.org/10.1109/CSITSS47250.2019.9031054 DOI: https://doi.org/10.1109/CSITSS47250.2019.9031054

[32] Y. Zhang, L. Wang, and Y. Duan, "Agricultural information dissemination using ICTs: A review and analysis of information dissemination models in China," Information Processing in Agriculture, vol. 3, no. 1, pp. 17–29, 2016. DOI: https://doi.org/10.1016/j.inpa.2015.11.002

[33] M. I. Gómez, "Fertilización estratégica en el manejo eficiente de la variedad nutricional de los cultivos," Seminario de investigación, Curso fertilidad de suelo, Universidad Nacional de Colombia, Bogotá DC, 2003, p. 41.

[34] C. M. Oliveira, A. M. Auad, S. M. Mendes, and M. R. Frizzas, "Crop losses and the economic impact of insect pests on Brazilian agriculture," Crop Protection, vol. 56, pp. 50–54, 2014. https://doi.org/10.1016/j.cropro.2013.10.022 DOI: https://doi.org/10.1016/j.cropro.2013.10.022

[35] J. M. Delgado, C. Giraldo, A. F. Millán, C. Zúñiga, and J. Abadía, "Desarrollo de un software Web y Móvil para la gestión de información de campo de cultivos agrícolas (AgrocomM)," Sistemas & Telemática, vol. 4, no. 8, pp. 113–124, 2006. https://doi.org/10.18046/syt.v4i8.969 DOI: https://doi.org/10.18046/syt.v4i8.969

[36] K. Lagos-Ortiz, J. Medina-Moreira, A. Alarcón-Salvatierra, M. F. Morán, J. del Cioppo-Morstadt, and R. Valencia-García, "Decision support system for the control and monitoring of crops," in Proceedings of the 2nd International Conference on ICTs in Agronomy and Environment, Cham, Springer International Publishing, Dec. 2018, pp. 20–28. DOI: https://doi.org/10.1007/978-3-030-10728-4_3

[37] A. K. Sahoo, C. Pradhan, and H. Das, "Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making," in Nature Inspired Computing for Data Science, pp. 201–212, 2020. https://doi.org/10.1007/978-3-030-33820-6_8 DOI: https://doi.org/10.1007/978-3-030-33820-6_8

[38] K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, "Machine learning in agriculture: A review," Sensors, vol. 18, no. 8, p. 2674, 2018. [Online]. Available: https://doi.org/10.3390/s18082674 DOI: https://doi.org/10.3390/s18082674

[39] E. J. Coopersmith, B. S. Minsker, C. E. Wenzel, and B. J. Gilmore, "Machine learning assessments of soil drying for agricultural planning," Computers and Electronics in Agriculture, vol. 104, pp. 93–104, 2014. https://doi.org/10.1016/j.compag.2014.04.004 DOI: https://doi.org/10.1016/j.compag.2014.04.004

[40] A. Morellos, X. E. Pantazi, D. Moshou, T. Alexandridis, R. Whetton, G. Tziotzios, and A. M. Mouazen, "Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy," Biosystems Engineering, vol. 152, pp. 104–116, 2016. https://doi.org/10.1016/j.biosystemseng.2016.04.018 DOI: https://doi.org/10.1016/j.biosystemseng.2016.04.018

[41] B. Nahvi, J. Habibi, K. Mohammadi, S. Shamshirband, and O. S. Al Razgan, "Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature," Computers and Electronics in Agriculture, vol. 124, pp. 150–160, 2016. https://doi.org/10.1016/j.compag.2016.03.025 DOI: https://doi.org/10.1016/j.compag.2016.03.025

[42] A. L. Johann, A. G. de Araújo, H. C. Delalibera, and A. R. Hirakawa, "Soil moisture modeling based on stochastic behavior of forces on a no-till chisel opener," Computers and Electronics in Agriculture, vol. 121, pp. 420–428, 2016. https://doi.org/10.1016/j.compag.2015.12.020 DOI: https://doi.org/10.1016/j.compag.2015.12.020

[43] Y. Bellini Saibene, J. Caldera, and L. Ramos, "Cosechando datos: desarrollos para la agricultura en la era digital," Electronic Journal of SADIO, vol. 19, 2020.

[44] Y. Bellini Saibene, "Propuesta para una infraestructura de datos agropecuarios del Instituto Nacional de Tecnología Agropecuaria (INTA)," in Simposio Argentino de GRANDES DATOS (AGRANDA 2016)-JAIIO 45, Tres de Febrero, Nov. 2016.

[45] Y. Bellini Saibene, M. Farrell, and H. Lorda, Relevamiento y análisis de la superficie de las explotaciones agropecuarias en el este de La Pampa, EEA INTA Anguil, 31 p., 2004. https://doi.org/10.17605/OSF.IO/3SC9X

[46] A. Kumar, S. Sarkar, and C. Pradhan, "Recommendation system for crop identification and pest control technique in agriculture," in 2019 International Conference on Communication and Signal Processing (ICCSP), Apr. 2019, pp. 0185–0189. [Online]. Available: https://doi.org/10.1109/ICCSP.2019.8697926 DOI: https://doi.org/10.1109/ICCSP.2019.8698099

[47] A. Gour, "Crop Recommendation and Pest Control Technique in Agriculture Using Machine Learning," International Journal of Electrical Engineering and Technology (IJEET), vol. 11, pp. 390–397, 2020.

[48] P. S. Vijayabaskar, R. Sreemathi, and E. Keertanaa, "Crop prediction using predictive analytics," in 2017 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), Mar. 2017, pp. 370–373. [Online]. Available: https://doi.org/10.1109/ICCPEIC.2017.8290418 DOI: https://doi.org/10.1109/ICCPEIC.2017.8290395

[49] Z. L. D. Hernández, J. O. B. Amaya, and J. A. A. Cuevas, Cultivo de arroz en la cuenca media y baja del río Cravo Sur: el reto de conservar y producir, vol. 352, 2020. DOI: https://doi.org/10.22490/9789586518130.11

[50] D. Y. R. Flórez, J. D. P. R. Tenjo, and M. H. Cáceres, "Marco metodológico para el desarrollo de aplicaciones móviles para el sector arrocero [Methodological framework for mobile application development for agricultural sector and rice]," Ventana Informática, no. 30, 2014. https://doi.org/10.30554/ventanainform.30.289.2014 DOI: https://doi.org/10.30554/ventanainform.30.289.2014

[51] A. O. Beltran, "Plataformas tecnológicas en la Agricultura 4.0: Una mirada al desarrollo en Colombia" Journal of Computer and Electronic Science, Theory and Applications, vol. 3, no. 1, pp. 9–18, 2022. https://doi.org/10.17981/cesta.03.01.2022.02 DOI: https://doi.org/10.17981/cesta.03.01.2022.02

[52] N. Berberian, J. Rosas, F. Pérez de Vida, M. Marella, and F. Massa, "Predicción de rendimiento en chacras: ¿qué es importante?," 2020. http://doi.org/10.35676/INIA/ST.257 DOI: https://doi.org/10.35676/INIA/ST.257

[53] FAO, "Plataforma Internacional para la Alimentación y la Agricultura Digitales," CL 164/9, Jun. 2020. [En Línea]. Disponible: https://www.fao.org/3/nd058es/nd058es.pdf

Published

2025-09-11

Issue

Section

Artículo Originales

How to Cite

Díaz Leal, N. R., Gómez Llanez, C. Y. . ., & Valenzuela-Balcázar, I. G. . (2025). State of the Art of Agrotechnological Tools in the Rice Farming System. Mundo FESC Journal, 15(32). https://doi.org/10.61799/2216-0388.1664