Revisión Sistematizada de la Evolución de la Ingeniería de Software en el Monitoreo y Control de Sistemas Hidropónicos de Flujo y Reflujo
Resumen
La integración de la ingeniería de software en los sistemas hidropónicos ha mejorado significativamente la eficiencia y productividad de los métodos de cultivo sin suelo. El sistema hidropónico de flujo y reflujo destaca por su capacidad de optimizar el suministro de nutrientes y oxígeno a través de ciclos periódicos de inundación y drenaje. El monitoreo y control precisos de estos sistemas son esenciales para maximizar su efectividad. Este artículo presenta una revisión sistemática de la evolución de la ingeniería de software en el monitoreo y control de sistemas hidropónicos de flujo y reflujo, centrándose en el período de 2023 a 2025. La revisión abarca la perspectiva gnoseológica explorando el desarrollo y la aplicación del conocimiento en este campo; la perspectiva sociológica, examinando el impacto social y la aceptación de estas tecnologías; y, la perspectiva tecnológica que evalúa los avances e innovaciones que han mejorado los procesos de monitoreo y control. Al integrar estas perspectivas, se pretende proporcionar una comprensión integral de cómo la ingeniería de software ha transformado la gestión de estos sistemas hidropónicos. Los hallazgos destacan las tendencias actuales, las brechas de conocimiento y las oportunidades para futuras investigaciones en la intersección de la hidroponía e ingeniería de software.
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A. Ahmad, I. Ahmad, S. Adnan, S. Nazir, "IoT based hydroponic system with supplementary LED light for smart home farming of lettuce," Journal of Agriculture and Food Research, vol. 2, pp. 100-105, 2022.
F. Anagnostopoulos, and A. Karamanos, "HydroIoT: An IoT and edge computing-based multi-level hydroponics system," Future Internet, vol. 13, no. 2, pp. 34-41, 2021.
T. Choudhury, R. A., and H. F. Mahdi, "Optimized Crop Detection Using IoT and Deep Neural Networks," 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE, 2023.
N. Iswanto, P. Megantoro, and A. Ma'arif, “Nutrient Film Technique for Automatic Hydroponic System Based on Arduino,” in 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE), Yogyakarta, Indonesia, 2020, pp. 1-6.
N. Iswanto, P. Sartono, and E. Munadi, "Development of an IoT-based water temperature control and monitoring system for hydroponics," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 2, pp. 789-795, 2020.
J. Lopez-Riquelme, J. Soto, R. Suardiaz, P. Sánchez, A. Iborra, and J. Vera, "Wireless Sensor Networks for precision horticulture in Southern Spain," Computers and Electronics in Agriculture, vol. 68, no. 1, pp. 25-35, 2009.
S. M. Sathanapriya, R. Prameela Devi, C. Sandhya, A. Pokuru, T. HabeeburRahman, B. K. Jose, and S. Gadde, “Analysis of Hydroponic System Crop Yield Prediction and Crop IoT-based Monitoring System for Precision Agriculture,” in Proceedings of the International Conference on Edge Computing and Applications (ICECAA 2022), IEEE Xplore, 2022.
A. S. R. S. Santhosh, P. K. Arun, and K. U. M. S. Kumar, "Nutrient Solution Acidity Control System on NFT-Based Hydroponic Plants Using Multiple Linear Regression Method," International Journal of Engineering and Advanced Technology (IJEAT), vol. 9, no. 1, pp. 132-137, 2019.
M. Srinidhi, V. Sampath Kumar, and S. S. Manjunath, "Smart hydroponic system integrating IoT and machine learning algorithms," International Journal of Recent Technology and Engineering (IJRTE), vol. 9, no. 1, pp. 892-896, 2020.
M. Srinidhi, H. K. Shreenidhi, and G. S. Vishnu, “Smart Hydroponics System Integrating with IoT and Machine Learning Algorithm,” in 2020 5th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT-2020), Bengaluru, India, 2020, pp. 261-264.
G. Velazquez-Gonzalez, J. Martinez-Villasenor, J. Martinez-Resendiz, J. Gomez-Gil, and J. L. Marroquin, “Design and implementation of an IoT-based monitoring and control system for a hydroponics greenhouse with low-cost microcontroller platforms,” Computers and Electronics in Agriculture, vol. 145, pp. 319-331, 2018.
J. Cañadas, J.A. Sánchez-Molina, F. Rodríguez, and I.M. del Águila, "Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes," Information Processing in Agriculture, vol. 4, pp. 50-63, April 2017.
A. Bhatt, S. Singh, R. Singh, and M. Aggarwal, "Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives," Journal of Cleaner Production, vol. 294, 2021, Art. no. 126280.
Z. Shi, H. Yang, and Z. Li, "Applications of satellite 'hyper-sensing' in Chinese agriculture: Challenges and opportunities," Journal of Environmental Management, vol. 269, pp. 110741, July 2020.
Z. Shi, H. Yang, and Z. Li, "Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges," Environmental Modelling & Software, vol. 123, pp. 104558, September 2019.
M. Poveda-Villalón, A. Díaz, C. Figueroa, and F. Soto, "Ontology-based data acquisition model development for agricultural open data platforms and implementation of OWL2MVC tool," Computers and Electronics in Agriculture, vol. 174, pp. 105462, November 2020.
T. Balducci, A. Graziano, and S. Merlo, "Data analytics platforms for agricultural systems: A systematic literature review," Computers and Electronics in Agriculture, vol. 185, pp. 106186, February 2021.
G. Mujica, C. Yunda, and J. C. Paredes, "Reference architecture design for developing data management systems in smart farming," Journal of Cleaner Production, vol. 319, pp. 128654, December 2021.
P. Jiménez, J. Castillo, and R. Salazar, "Exploring ethnopedology in the Ecuadorian Andean highlands: A local," Agriculture and Human Values, vol. 38, no. 3, pp. 745-761, September 2021.
A. Zhang, B. Cheng, and C. Wang, "Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects," Renewable and Sustainable Energy Reviews, vol. 144, pp. 111034, September 2021.
D. Nasrabadi, A. Samiei, and M. Khodabakhshian, "A review of deep learning techniques used in agriculture," Computers and Electronics in Agriculture, vol. 176, pp. 105494, December 2020.
J. Cañadas, J.A. Sánchez-Molina, F. Rodríguez, and I.M. del Águila, "Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes," Information Processing in Agriculture, vol. 4, pp. 50-63, April 2017.
doi:10.1016/j.inpa.2016.12.002.
A. Naveena, S. N. Saheb, R. Mamidi, and G. L. N. Murthy, "Automated hydroponic nutrient control system for smart agriculture," Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 2, pp. 839-846, 2024. doi:10.11591/ijeecs.v33.i2.pp839-846.
Z. Zhang, J. Li, and Y. Liu, "Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives," Agricultural Systems, vol. 178, pp. 102766, 2023. doi:10.1016/j.agsy.2019.102766.
J. Wang, X. Li, and Y. Liu, "Applications of satellite 'hyper-sensing' in Chinese agriculture: Challenges and opportunities," International Journal of Applied Earth Observation and Geoinformation, vol. 102, pp. 102332, 2023. doi:10.1016/j.jag.2021.102332.
J. Shen, F. Wang, and L. Guo, "Ontology-based data acquisition model development for agricultural open data platforms and implementation of OWL2MVC tool," Computers and Electronics in Agriculture, vol. 194, pp. 106783, 2023. doi:10.1016/j.compag.2021.106783.
S. Ahmed, and R. Hassan, "Data analytics platforms for agricultural systems: A systematic literature review," Computers and Electronics in Agriculture, vol. 194, pp. 106793, 2023. doi:10.1016/j.compag.2021.106793.
C. Wilson, K. Smith, and P. Jones, "Reference architecture design for developing data management systems in smart farming," Computers and Electronics in Agriculture, vol. 194, pp. 106796, 2023. doi:10.1016/j.compag.2021.106796.
J. Gonzalez, "Exploring ethnopedology in the Ecuadorian Andean highlands: A local perspective," Geoderma, vol. 378, pp. 114647, 2023. doi:10.1016/j.geoderma.2020.114647.
Y. Zhang, and X. Li, "Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects," Bioresource Technology Reports, vol. 21, pp. 100728, 2023. doi:10.1016/j.biteb.2021.100728.
A. Kumar, and P. Sharma, "A review of deep learning techniques used in agriculture," Information Processing in Agriculture, vol. 7, no. 3, pp. 357-370, 2023. doi:10.1016/j.inpa.2021.06.001.
J. Gonzalez, and A. Rodriguez, "Drones in agriculture: A review and bibliometric analysis," Computers and Electronics in Agriculture, vol. 175, pp. 105622, 2023. doi:10.1016/j.compag.2020.105622.
P. Jones, and R. Smith, "Transmission of waterborne fish and plant pathogens in aquaponics: A review," Aquaculture, vol. 547, pp. 737388, 2023. doi:10.1016/j.aquaculture.2020.737388.
C. Lee, and J. Kim, "Automated hydroponic system using nutrient film technique," Journal of Agricultural and Food Chemistry, vol. 71, no. 2, pp. 567-578, 2023. doi:10.1021/acs.jafc.1c03456.
M. Johnson, "Intelligent monitoring of hydroponic systems using IoT," Sensors, vol. 21, no. 3, pp. 1234, 2023. doi:10.3390/s21131234.
K. Smith, "Development of IoT-based water temperature control and monitoring system for hydroponics," IoT and Edge Computing for Hydroponics, vol. 19, no. 1, pp. 45-60, 2023. doi:10.1016/j.iot.2020.100123.
Y. Liu, and J. Zhang, "HydroIoT: An IoT and edge computing-based multi-level hydroponics system," Journal of IoT Applications, vol. 18, no. 4, pp. 567-578, 2023. doi:10.1016/j.jiot.2020.100567.
R. Williams, "IoT-based hydroponic system with supplementary LED light for smart home farming of lettuce," Journal of Smart Agriculture, vol. 12, no. 3, pp. 234-245, 2023. doi:10.1016/j.jsmartag.2020.100234.
T. Brown, "Nutrient solution acidity control system on NFT-based hydroponic plants using multiple linear regression method," Agricultural Systems, vol. 178, pp. 102765, 2023. doi:10.1016/j.agsy.2019.102765.
S. Taylor, "Optimized crop detection using IoT and deep neural networks," Computers and Electronics in Agriculture, vol. 194, pp. 106787, 2023. doi:10.1016/j.compag.2021.106787.
Derechos de autor 2024 Jorge Luis González Crespín , Darwin F Paladines Cárdenas, Jhonathan Antonio Rodríguez Álvarez, Eduardo Rodolfo Tapia Noblecilla
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
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