Internet de las cosas para el monitoreo de factores ambientales agrícolas: mapeo sistemático
DOI:
https://doi.org/10.35381/i.p.v8i14.4909Palabras clave:
Agricultura inteligente, Internet de las cosas (IoT), Monitoreo ambiental agrícola, Sensores inalámbricos, (Tesauro UNESCO).Resumen
La aplicación del Internet de las Cosas (IoT) en la agricultura enfrenta diversos enfoques y desafíos debido a la variedad tecnológica y de factores ambientales monitoreados. Se planteó como problema general: ¿Cómo se han aplicado las tecnologías IoT para el monitoreo de factores ambientales en la producción agrícola durante los últimos cinco años? Para ello, se consultaron bases de datos indexadas (Scopus, IEEE Xplore, ScienceDirect y SAGE Journals), aplicando criterios de inclusión y exclusión bajo el enfoque PICOC. Se analizaron 30 estudios que revelaron que los factores más monitoreados son temperatura, humedad, pH, CO₂ y radiación solar. Las tecnologías predominantes incluyen sensores de bajo consumo, redes WSN y LoRaWAN, y plataformas como Raspberry Pi y ThingSpeak. Sin embargo, persisten limitaciones como dependencia de conectividad, falta de interoperabilidad y costos de implementación. Los hallazgos ofrecen una visión estructurada del estado del arte y orientan investigaciones hacia soluciones IoT sostenibles y eficientes.
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Aarthi, R., Sivakumar, D., y Mariappan, V. (2023). Smart Soil Property Analysis Using IoT: A Case Study Implementation in Backyard Gardening. Procedia Computer Science, 218, 2842–2851. https://doi.org/10.1016/J.PROCS.2023.01.255
Abu, N. S., Bukhari, W. M., Ong, C. H., Kassim, A. M., Izzuddin, T. A., Sukhaimie, M. N., Norasikin, M. A., y Rasid, A. F. A. (2022). Internet of Things applications in precision agriculture: A review. Journal of Robotics and Control (JRC), 3(3), Article 14159-338. https://doi.org/10.18196/jrc.v3i3.14159
Adami, D., Ojo, M. O., y Giordano, S. (2021). Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI. IEEE Access, 9, 132125–132139. https://doi.org/10.1109/ACCESS.2021.3114503
Alahmad, T., Neményi, M., y Nyéki, A. (2023). Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review. Agronomy, 13(10), 2603. https://doi.org/10.3390/agronomy13102603
Al Mamun, M. R., Ahmed, A. K., Upoma, S. M., Haque, M. M., y Ashik-E-Rabbani, M. (2025). IoT-enabled solar-powered smart irrigation for precision agriculture. Smart Agricultural Technology, 10, 100773. https://doi.org/10.1016/j.atech.2025.100773
Alumfareh, M. F., Humayun, M., Ahmad, Z., y Khan, A. (2024). An Intelligent LoRaWAN-based IoT Device for Monitoring and Control Solutions in Smart Farming through anomaly detection integrated with unsupervised machine learning. IEEE Access, 12, 119072-119086. https://doi.org/10.1109/ACCESS.2024.3450587
Bauer, J., y Aschenbruck, N. (2020). Towards a low-cost RSSI-based crop monitoring. ACM Transactions on Internet of Things, 1(4), 21. https://doi.org/10.1145/3393667
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report. Ver. 2.3. EBSE Technical Report. https://n9.cl/a2tfx
Cisternas, I., et al. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture, 176, 105626. https://doi.org/10.1016/j.compag.2020.105626
Eteng, I., Ugbe, C., y Oladimeji, S. (2022). Implementing smart farming using internet technology and data analytics: a prototype of a rice farm. Eastern-European Journal of Enterprise Technologies, 3(2), 48–62. https://doi.org/10.15587/1729-4061.2022.259113
Frandsen, T. F., Bruun Nielsen, M. F., Lindhardt, C. L., y Eriksen, M. B. (2020). Using the full PICO model as a search tool for systematic reviews resulted in lower recall for some PICO elements. Journal of Clinical Epidemiology, 127, 69–75. https://doi.org/10.1016/j.jclinepi.2020.07.005
Ferrarezi, R. S., y Peng, T. W. (2021). Smart System for Automated Irrigation Using Internet of Things Devices. HortTechnology, 31(6), 642–649. https://doi.org/10.21273/HORTTECH04860-21
Food and Agriculture Organization of the United Nations [FAO]. (2017). The future of food and agriculture: Trends and challenges. FAO. https://n9.cl/jrmgr
Garrido, M. C., Cadenas, J. M., Bueno-Crespo, A., Martínez-España, R., Giménez, J. G., y Cecilia, J. M. (2022). Evaporation Forecasting through Interpretable Data Analysis Techniques. Electronics (Switzerland), 11(4), 536. https://doi.org/10.3390/electronics11040536
Hachimi, C. El, Belaqziz, S., Khabba, S., Sebbar, B., Dhiba, D., y Chehbouni, A. (2023). Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture. Agriculture (Switzerland), 13(1), 95. https://doi.org/10.3390/agriculture13010095
Huda, S., Nogami, Y., Rahayu, M., Akada, T., Hossain, M. B., Musthafa, M. B., Jie, Y., y Anh, L. H. (2024). IoT-Enabled Plant Monitoring System with Power Optimization and Secure Authentication. Computers, Materials and Continua, 81(2), 3165–3187. https://doi.org/10.32604/CMC.2024.058144
Islam, M. R., Oliullah, K., Kabir, M. M., Alom, M., y Mridha, M. F. (2023). Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation. Journal of Agriculture and Food Research, 14, 100880. https://doi.org/10.1016/J.JAFR.2023.100880
Jamal, J., Azizi, S., Abdollahpouri, A., Ghaderi, N., Sarabi, B., Silva-Ordaz, A., y Castaño-Meneses, V. M. (2021). Monitoring rocket (Eruca sativa) growth parameters using the Internet of Things under supplemental LEDs lighting. Sensing and Bio-Sensing Research, 34, 100450. https://doi.org/10.1016/j.sbsr.2021.100450
Jin, X. B., Yu, X. H., Wang, X. Y., Bai, Y. T., Su, T. L., y Kong, J. L. (2020). Deep learning predictor for sustainable precision agriculture based on internet of things system. Sustainability (Switzerland), 12(4), 1433. https://doi.org/10.3390/su12041433
Kalimuthu, V. K., y PrabuPelavendran, M. J. (2024). Blockchain Based Secure Data Sharing in Precision Agriculture: a Comprehensive Methodology Incorporating Deep learning and Hybrid Encryption Model. Brazilian Archives of Biology and Technology, 67, 1–17. https://doi.org/10.1590/1678-4324-2024230858
Marcu, I., Drăgulinescu, A. M., Oprea, C., Suciu, G., y Bălăceanu, C. (2022). Predictive Analysis and Wine-Grapes Disease Risk Assessment Based on Atmospheric Parameters and Precision Agriculture Platform. Sustainability (Switzerland), 14(18), 11487. https://doi.org/10.3390/su141811487
Morchid, A., Oughannou, Z., Alami, R. El, Qjidaa, H., Jamil, M. O., y Khalid, H. M. (2024). Integrated internet of things (IoT) solutions for early fire detection in smart agriculture. Results in Engineering, 24, 103392. https://doi.org/10.1016/J.RINENG.2024.103392
Pragadeswaran, S., Vishnu, S., Surya, P., Kurup, V., y Tamilselvan, S. (2023). An investigation on real-time monitoring system for livestock and agriculture using IoT. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3(1), 102-109. https://doi.org/10.48175/ijarsct-8566
Peppi, L. M., Zauli, M., Manfrini, L., Grappadelli, L. C., De Marchi, L., y Traverso, P. A. (2023). Low-cost, high-resolution and no-manning distributed sensing system for the continuous monitoring of fruit growth in precision farming. Acta IMEKO, 12(2), 17. https://doi.org/10.21014/actaimeko.v12i2.1342
Placidi, P., Morbidelli, R., Fortunati, D., Papini, N., Gobbi, F., y Scorzoni, A. (2021). Monitoring soil and ambient parameters in the iot precision agriculture scenario: An original modeling approach dedicated to low-cost soil water content sensors. Sensors, 21(15), 5110. https://doi.org/10.3390/s21155110
Qayyum, K., Zaman, I., y Förster, A. (2020). H2O Sense: a WSN-based monitoring system for fish tanks. SN Applied Sciences, 2(10), 1643. https://doi.org/10.1007/s42452-020-03328-3
Rokade, A. I., Kadu, A. D., y Belsare, K. S. (2022). An Autonomous Smart Farming System for Computational Data Analytics using IoT. Journal of Physics: Conference Series, 2327(1), 012019. https://doi.org/10.1088/1742-6596/2327/1/012019
Satheswaran, N., Sri Pavithra, P., Selva Prabha, V., y Sugirtha, S. (2023). IoT based smart agriculture monitoring system project. International Journal for Research in Applied Science y Engineering Technology (IJRASET), 11(6), Article 2190.
Segrera Salom, G. A., Castro Ayala, D. F., y Galvis Sanmiguel, J. A. (2022). Sistema IoT flexible para el monitoreo de variables ambientales en aplicaciones agroindustriales. Pontificia Universidad Javeriana. https://repository.javeriana.edu.co/handle/10554/63717
Sami, M., Khan, S. Q., Khurram, M., Farooq, M. U., Anjum, R., Aziz, S., Qureshi, R., y Sadak, F. (2022). A Deep Learning-Based Sensor Modeling for Smart Irrigation System. Agronomy, 12(1), 212. https://doi.org/10.3390/agronomy12010212
Suresh, P., Aswathy, R. H., Arumugam, S., Albraikan, A. A., Al-Wesabi, F. N., Hilal, A. M., y Alamgeer, M. (2022). Iot with evolutionary algorithm based deep learning for smart irrigation system. Computers, Materials and Continua, 71(1), 1713–1728. https://doi.org/10.32604/cmc.2022.021789
Tsipis, A., Papamichail, A., Koufoudakis, G., Tsoumanis, G., Polykalas, S. E., y Oikonomou, K. (2020). Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves. AgriEngineering, 2(1), 175–205. https://doi.org/10.3390/agriengineering2010011
Tugnolo, A., Oliveira, H. M., Giovenzana, V., Fontes, N., Silva, S., Fernandes, C., Graça, A., Pampuri, A., Casson, A., Piteira, J., Freitas, P., Guidetti, R., y Beghi, R. (2025). Quantitative prediction of grape ripening parameters combining an autonomous IoT spectral sensing system and chemometrics. Computers and Electronics in Agriculture, 230, 109856. https://doi.org/10.1016/j.compag.2024.109856
Vandôme, P., Leauthaud, C., Moinard, S., Sainlez, O., Mekki, I., Zairi, A., y Belaud, G. (2023). Making technological innovations accessible to agricultural water management: Design of a low-cost wireless sensor network for drip irrigation monitoring in Tunisia. Smart Agricultural Technology, 4, 100227. https://doi.org/10.1016/J.ATECH.2023.100227
Zito, F., Giannoccaro, N. I., Serio, R., y Strazzella, S. (2024). Analysis and Development of an IoT System for an Agrivoltaics Plant. Technologies, 12(7), 106. https://doi.org/10.3390/technologies12070106
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Derechos de autor 2026 Gardyn Olivera-Ruiz, Edwin Jesús Vega-Ventocilla
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