Sensores y aplicaciones de IoT en entornos agrícolas: mapeo sistemático
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https://doi.org/10.5281/zenodo.15809119Palabras clave:
agricultura de precisión, desarrollo sostenible, sensoresResumen
La presente investigación realizó un mapeo sistemático de la literatura científica sobre sensores y aplicaciones de IoT en la agricultura, con el objetivo de identificar las principales líneas de desarrollo, sus beneficios y las barreras para su adopción. Se aplicó la metodología de Petersen y Kitchenham, utilizando el marco PICOC para diseñar la búsqueda en bases de datos como Scopus, IEEE Xplore, Taylor & Francis y Sage Journals, considerando estudios originales publicados entre 2019 y 2024. De 1,862 registros iniciales, se seleccionaron 59 artículos tras aplicar criterios de inclusión relacionados con descripción técnica, análisis de ventajas y limitaciones, e impacto en sostenibilidad. El análisis bibliométrico mostró un crecimiento constante en las publicaciones, con Asia y Europa como principales focos. La síntesis temática reveló que los sistemas de riego inteligente concentran el 28 % de las contribuciones, seguidos por el monitoreo de cultivos (17 %) y los sistemas de alerta temprana (10 %). En contraste, áreas como fertilización inteligente y conservación de la biodiversidad están poco exploradas. Los estudios reportan mejoras significativas en eficiencia hídrica y energética, y reducción de la huella ambiental. Sin embargo, persisten limitaciones técnicas como la vida útil limitada de baterías, calibraciones frecuentes y heterogeneidad de protocolos, además de escasa evaluación socioeconómica. En conclusión, aunque el IoT tiene un gran potencial para una agricultura más sostenible y resiliente, su impacto a gran escala requiere avances en autonomía energética, estandarización de comunicaciones y estudios costo-beneficio que faciliten su adopción en pequeñas explotaciones rurales.
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Abdelmoneim, A. A., Khadra, R., Elkamouh, A., Derardja, B., & Dragonetti, G. (2024). Towards affordable precision irrigation: An experimental comparison of weather-based and soil water potential-based irrigation using low-cost IoT-tensiometers on drip irrigated lettuce. Sustainability, 16(1), 306. https://doi.org/10.3390/su16010306
Abid, M. A., Amjad, M., Munir, K., Siddique, H. U. R., & Jurcut, A. D. (2024). IoT-based smart biofloc monitoring system for fish farming using machine learning. IEEE Access, 12, 86333–86345. https://doi.org/10.1109/ACCESS.2024.3384263
Alghazzawi, D., Bamasaq, O., Bhatia, S., Kumar, A., Dadheech, P., & Albeshri, A. (2021). Congestion control in cognitive IoT-based WSN network for smart agriculture. IEEE Access, 9, 151401–151420. https://doi.org/10.1109/ACCESS.2021.3124791
Alharbi, H. A., & Aldossary, M. (2021). Energy-efficient edge-fog-cloud architecture for IoT-based smart agriculture environment. IEEE Access, 9, 110480–110492. https://doi.org/10.1109/ACCESS.2021.3101397
Alves, R., & Matos, P. (2023). A solution to prevent and minimize the consequences of accidents with farm tractors in the context of mountainous regions with low population density. Sensors, 23(18), 7811. https://doi.org/10.3390/s23187811
Ammad Uddin, M., Ayaz, M., Aggoune, E. H. M., Mansour, A., & Le Jeune, D. (2019). Affordable broad agile farming system for rural and remote area. IEEE Access, 7, 127098–127116. https://doi.org/10.1109/ACCESS.2019.2937881
Arafa, Y., El-Gindy, A. G. M., El-Shirbeny, M., Bourouah, M., Abd-ElGawad, A. M., Rashad, Y. M., Hafez, M., & Youssef, M. A. (2024). Improving the spatial deployment of the soil moisture sensors in smart irrigation systems using GIS. Cogent Food and Agriculture, 10(1), 2361124. https://doi.org/10.1080/23311932.2024.2361124
Armiento, S., Meder, F., & Mazzolai, B. (2023). Device for simultaneous wind and raindrop energy harvesting operating on the surface of plant leaves. IEEE Robotics and Automation Letters, 8(4), 2269–2276. https://doi.org/10.1109/LRA.2023.3250006
Arunachalam, A., & Andreasson, H. (2022). RaspberryPi-Arduino (RPA) powered smart mirrored and reconfigurable IoT facility for plant science research. Internet Technology Letters, 5(1), e272. https://doi.org/10.1002/itl2.272
Ayu Nuning Farida Afiatna, F., & Muflihah, N. (2024). The monitoring system of soil pH factor using IoT-webserver-android and machine learning: A case study. Int. J. Adv. Sci. Eng. Inf. Technol., 14(1), 118–130. https://doi.org/10.18517/ijaseit.14.1.18745
Baljon, M. (2024). Revolutionizing Saudi Arabia’s agriculture: The IoT transformation of water management. Journal of Advanced Research in Applied Sciences and Engineering Technology, 36(1), 217–240. https://doi.org/10.37934/araset.36.1.217240
Behjati, M., Mohd Noh, A. B., Alobaidy, H. A. H., Zulkifley, M. A., Nordin, R., & Abdullah, N. F. (2021). LoRa communications as an enabler for internet of drones towards large-scale livestock monitoring in rural farms. Sensors, 21(15), 5044. https://doi.org/10.3390/s21155044
Chittoor, P. K., Chokkalingam, B., Verma, R., & Mihet-Popa, L. (2023). An assessment of shortest prioritized path-based bidirectional wireless charging approach toward smart agriculture. IEEE Access, 11, 123742–123755. https://doi.org/10.1109/ACCESS.2023.3329976
Corbari, C., Paciolla, N., Ben Charfi, I., Skokovic, D., Sobrino, J. A., & Woods, M. (2022). Citizen science supporting agricultural monitoring with hundreds of low-cost sensors in comparison to remote sensing data. European Journal of Remote Sensing, 55(1), 388–408. https://doi.org/10.1080/22797254.2022.2084643
Dash, S. S., & Kumar, P. (2024). Farmers’ toolkit: Deep learning in weed detection and precision crop & fertilizer recommendations. BIO Web of Conferences, 82, 05012. https://doi.org/10.1051/bioconf/20248205012
Debangshi, U. (2021). Drone-applications in agriculture. https://doi.org/10.5281/zenodo.5554734
Dong, Y., Werling, B., Cao, Z., & Li, G. (2024). Implementation of an in-field IoT system for precision irrigation management. Frontiers in Water, 6, 1353597. https://doi.org/10.3389/frwa.2024.1353597
Effah, E., Thiare, O., & Wyglinski, A. M. (2024). Hardware evaluation of cluster-based agricultural IoT network. IEEE Access, 12, 33628–33651. https://doi.org/10.1109/ACCESS.2024.3370230
FAO. (s.f.). El futuro de la alimentación y la agricultura: Tendencias y desafíos. Panorama General. www.fao.org/faostat/es/#home
Florea, A., Popa, D. I., Morariu, D., Maniu, I., Berntzen, L., & Fiore, U. (2024). Digital farming based on a smart and user-friendly IoT irrigation system: A conifer nursery case study. IET Cyber-Physical Systems: Theory and Applications, 9(2), 150–168. https://doi.org/10.1049/cps2.12054
Frandsen, T. F., Bruun Nielsen, M. F., Lindhardt, C. L., & 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
Glória, A., Cardoso, J., & Sebastião, P. (2021). Sustainable irrigation system for farming supported by machine learning and real-time sensor data. Sensors, 21(9), 3079. https://doi.org/10.3390/s21093079
Haseeb, K., Din, I. U., Almogren, A., & Islam, N. (2020). An energy efficient and secure IoT-based WSN framework: An application to smart agriculture. Sensors, 20(7), 2081. https://doi.org/10.3390/s20072081
Holtorf, L., Titov, I., Daschner, F., & Gerken, M. (2023). UAV-based wireless data collection from underground sensor nodes for precision agriculture. AgriEngineering, 5(1), 338–354. https://doi.org/10.3390/agriengineering5010022
Ibn Dahou Idrissi, A., Abouabdillah, A., Chikhaoui, M., & Bouabid, R. (2024). Low-cost IoT-based monitoring system for precision agriculture. E3S Web of Conferences, 492, 01003. https://doi.org/10.1051/e3sconf/202449201003
Irwanto, F., Hasan, U., Lays, E. S., De La Croix, N. J., Mukanyiligira, D., Sibomana, L., & Ahmad, T. (2024). IoT and fuzzy logic integration for improved substrate environment management in mushroom cultivation. Smart Agricultural Technology, 7, 100427. https://doi.org/10.1016/j.atech.2024.100427
Islam, M. R., Oliullah, K., Kabir, M. M., Alom, M., & Mridha, M. F. (2023a). 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
Islam, M. R., Oliullah, K., Kabir, M. M., Alom, M., & Mridha, M. F. (2023b). 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
Jadhav, N., Rajnivas, B., Subapriya, V., Sivaramakrishnan, S., Premalatha, S., & Poongodi, P. (2024). Enhancing crop growth efficiency through IoT-enabled smart farming system. EAI Endorsed Transactions on Internet of Things, 10. https://doi.org/10.4108/eetiot.4604
Jia, X., Huang, Y., Wang, Y., & Sun, D. (2019). Research on water and fertilizer irrigation system of tea plantation. International Journal of Distributed Sensor Networks, 15(3). https://doi.org/10.1177/1550147719840182
Jimale, A. D., Abdullahi, M. O., Ahmed, Y. A., & Nageye, A. Y. (2023). Towards sustainable farming in Somalia: Integrating IoT for improved resource management. SSRG International Journal of Electrical and Electronics Engineering, 10(9), 95–101. https://doi.org/10.14445/23488379/IJEEE-V10I9P109
Kaur, A., Bhatt, D. P., & Raja, L. (2024). Developing a hybrid irrigation system for smart agriculture using IoT sensors and machine learning in Sri Ganganagar, Rajasthan. Journal of Sensors, 2024, 6676907. https://doi.org/10.1155/2024/6676907
Kitchenham, B. & Charters, S. (2007) Guidelines for Performing Systematic Literature Reviews in Software Engineering, Technical Report EBSE 2007-001, Keele University and Durham University Joint Report. https://www.researchgate.net/publication/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering
Kuang, L., Shi, P., Hua, C., Chen, B., & Zhu, H. (2020). An enhanced extreme learning machine for dissolved oxygen prediction in wireless sensor networks. IEEE Access, 8, 198730–198739. https://doi.org/10.1109/ACCESS.2020.3033455
Kumar, G. K., Bangare, M. L., Bangare, P. M., Kumar, C. R., Raj, R., Arias-Gonzáles, J. L., Omarov, B., & Mia, M. S. (2024). Internet of things sensors and support vector machine integrated intelligent irrigation system for agriculture industry. Discover Sustainability, 5(1), 6. https://doi.org/10.1007/s43621-024-00179-5
Lavanya, P., & Muthu Mayil, K. (2019). IoT-based wireless sensors for agriculture monitoring. International Journal of Recent Technology and Engineering, 8(2), 177–181. https://doi.org/10.35940/ijrte.B1033.0782S419
Levin, S. (2024). Intelligent monitoring and management in the agro-industrial complex. E3S Web of Conferences, 539, 02016. https://doi.org/10.1051/e3sconf/202453902016
Liang, W. Y., & Juang, J. G. (2022). Application of image identification to UAV control for cage culture. Science Progress, 105(4). https://doi.org/10.1177/00368504221135450
Mishra, S., Volety, D. R., Bohra, N., Alfarhood, S., & Safran, M. (2023). A smart and sustainable framework for millet crop monitoring equipped with disease detection using enhanced predictive intelligence. Alexandria Engineering Journal, 83, 298–306. https://doi.org/10.1016/j.aej.2023.10.041
Mohammed, M., Riad, K., & Alqahtani, N. (2021). Efficient IoT-based control for a smart subsurface irrigation system to enhance irrigation management of date palm. Sensors, 21(12), 3942. https://doi.org/10.3390/s21123942
Mummaneni, S., Sappa, T. S., & Katakam, V. G. D. (2024). Enhancing crop health through digital twin for disease monitoring and nutrient balance. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska, 14(1), 57–62. https://doi.org/10.35784/iapgos.5626
Munaganuri, R. K., & Rao, Y. N. (2024). PAMICRM: Improving precision agriculture through multimodal image analysis for crop water requirement estimation using multidomain remote sensing data samples. IEEE Access, 12, 52815–52836. https://doi.org/10.1109/ACCESS.2024.3386552
Nieman, B., Johnson, C. S., Pearce, M., Marcrum, T., Thorne, M. C., Ashby, C., & Van Neste, C. W. (2024). Through the soil long range wireless power transfer for agricultural IoT networks. IEEE Transactions on Industrial Electronics, 71(2), 2090–2099. https://doi.org/10.1109/TIE.2023.3250743
Pérez-Pons, M. E., Plaza-Hernández, M., Alonso, R. S., Parra-Domínguez, J., & Prieto, J. (2021). Increasing profitability and monitoring environmental performance: A case study in the agri-food industry through an edge-IoT platform. Sustainability, 13(1), 1–16. https://doi.org/10.3390/su13010283
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64, 1–18. https://doi.org/10.1016/J.INFSOF.2015.03.007
Pham, V. B., Diep, T. T., Fock, K., & Nguyen, T. S. (2021). Using the Internet of Things to promote alternate wetting and drying irrigation for rice in Vietnam’s Mekong Delta. Agronomy for Sustainable Development, 41(3), 43. https://doi.org/10.1007/s13593-021-00705-z
Pineda-Castro, D., Diaz, H., Soto, J., & Urban, M. O. (2024). LysipheN: A gravimetric IoT device for near real-time high-frequency crop phenotyping: A case study on common beans. Plant Methods, 20(1), 39. https://doi.org/10.1186/s13007-024-01170-x
Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., & Dragana, C. (2020). Advanced UAV–WSN system for intelligent monitoring in precision agriculture. Sensors, 20(3), 817. https://doi.org/10.3390/s20030817
Postolache, S., Sebastião, P., Viegas, V., Postolache, O., & Cercas, F. (2023). IoT-based systems for soil nutrients assessment in horticulture. Sensors, 23(1), 403. https://doi.org/10.3390/s23010403
Prasad, R., Tiwari, R., & Srivastava, A. K. (2023). Internet of Things-based fuzzy logic controller for smart soil health monitoring: A case study of semi-arid regions of India. Engineering Proceedings, 58(1), 85. https://doi.org/10.3390/ecsa-10-16208
Priya, G. L., Baskar, C., Deshmane, S. S., Adithya, C., & Das, S. (2023). Revolutionizing holy-basil cultivation with AI-enabled hydroponics system. IEEE Access, 11, 82624–82639. https://doi.org/10.1109/ACCESS.2023.3300912
Putri, R. E., Lestari, N. U., Ifmalinda, Arlius, F., Putri, I., & Hasan, A. (2024). Monitoring and controlling system of smart mini greenhouse based on Internet of Things (IoT) for spinach plant (Amaranthus sp.). International Journal on Advanced Science, Engineering and Information Technology, 14(1), 131–136. https://doi.org/10.18517/ijaseit.14.1.18408
Ram, C. R. S., Ravimaran, S., Krishnan, R. S., Julie, E. G., Robinson, Y. H., Kumar, R., Son, L. H., Thong, P. H., Thanh, N. Q., & Ismail, M. (2020). Internet of green things with autonomous wireless wheel robots against greenhouses and farms. International Journal of Distributed Sensor Networks, 16(6). https://doi.org/10.1177/1550147720923477
Sajak, A. A. B., Ahmad, M. N. I., & Dao, H. (2024). Green IoT based on tropical weather: The impact of energy harvesting in wireless sensor network. Journal of Advanced Research in Applied Sciences and Engineering Technology, 40(1), 35–44. https://doi.org/10.37934/araset.40.1.3544
Satra, R., Hadi, M. S., Sujito, Febryan, Fattah, M. H., & Busaeri, S. R. (2024). IoAT: Internet of aquaculture things for monitoring water temperature in tiger shrimp ponds with DS18B20 sensors and WeMos D1 R2. Journal of Robotics and Control (JRC), 5(1), 62–71. https://doi.org/10.18196/jrc.v5i1.18470
Setyawan, D. Y., Warsito, Marjunus, R., & Sumaryo. (2024). A novel controlling system for smart farming-based Internet of Things (IoT). International Journal of Advanced Computer Science and Applications, 15(5), 630–641. https://doi.org/10.14569/IJACSA.2024.0150563
Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415
Tagarakis, A. C., Kateris, D., Berruto, R., & Bochtis, D. (2021). Low-cost wireless sensing system for precision agriculture applications in orchards. Applied Sciences, 11(13), 5858. https://doi.org/10.3390/app11135858
Taneja, M., Byabazaire, J., Jalodia, N., Davy, A., Olariu, C., & Malone, P. (2020). Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171, 105286. https://doi.org/10.1016/J.COMPAG.2020.105286
Taparci, E., Olcay, K., Akmandor, M. O., Kabakulak, B., Sarioglu, B., & Gokdel, Y. D. (2024). A mathematical programming approach for IoT-enabled, energy-efficient heterogeneous wireless sensor network design and implementation. Sensors, 24(5), 1457. https://doi.org/10.3390/s24051457
Ting, Y. T., & Chan, K. Y. (2024). Optimising performances of LoRa based IoT enabled wireless sensor network for smart agriculture. Journal of Agriculture and Food Research, 16, 101093. https://doi.org/10.1016/j.jafr.2024.101093
Tiurlikova, A., Stepanov, N., & Mikhaylov, K. (2019). Wireless power transfer from unmanned aerial vehicle to low-power wide area network nodes: Performance and business prospects for LoRaWAN. International Journal of Distributed Sensor Networks, 15(11). https://doi.org/10.1177/1550147719888165
Ullah, R., Abbas, A. W., Ullah, M., Khan, R. U., Khan, I. U., Aslam, N., & Aljameel, S. S. (2021). EEWMP: An IoT-based energy-efficient water management platform for smart irrigation. Scientific Programming, 2021, 5536884. https://doi.org/10.1155/2021/5536884
Wibisono, A. B., & Jayadi, R. (2024). Experimental IoT system to maintain water quality in catfish pond. International Journal of Advanced Computer Science and Applications, 15(3), 393–399. https://doi.org/10.14569/IJACSA.2024.0150340
Wu, Y., Yang, Z., & Liu, Y. (2023). Internet-of-things-based multiple-sensor monitoring system for soil information diagnosis using a smartphone. Micromachines, 14(7), 1395. https://doi.org/10.3390/mi14071395
Yau, S. R., Jalani, J., Sadun, A. S., Rejab, S. M., & John, J. (2025). Development of an aquaponics farming technology system using Arduino based on Internet of Things. Journal of Advanced Research in Applied Sciences and Engineering Technology, 45(2), 11–24. https://doi.org/10.37934/araset.45.2.1124
Yin, D., Wang, Y., & Huang, Y. (2023). Predicting soil moisture content of tea plantation using support vector machine optimized by arithmetic optimization algorithm. Journal of Algorithms and Computational Technology, 17. https://doi.org/10.1177/17483026221151198
Zhang, C., & Liu, Z. (2019). Application of big data technology in agricultural Internet of Things. International Journal of Distributed Sensor Networks, 15(10). https://doi.org/10.1177/1550147719881610
Zito, F., Giannoccaro, N. I., Serio, R., & 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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