Design and deployment of an IoT-based monitoring system for hydroponic crops
Main Article Content
Abstract
The IoT is a technological trend, it makes possible intelligent systems between connected things, its application is founded in different fields, one of them is agriculture, where the use of new techniques such as hydroponics are booming. It is important to address this area because the world population will reach approximately 9.6 billion inhabitants by 2050, therefore, to meet this demand, the agricultural industrial pace needs to be even faster and more precise. Moreover, the increase in ambient temperature and climate changes due to global warming are also negatively affecting agricultural production. In this research, a scalable IoT monitoring system based on Sigfox technology with 89.37% prediction capabilities through neural networks is presented for agricultural applications. An effective four-layer architecture consisting of perception, network, middleware, and application is provided. For validation, the system was built, experimentally tested and validated by monitoring temperature, humidity and nutrient recirculation control, in a hydroponic system in the city of Loja-Ecuador, for five months. The developed system is intelligent enough to provide the appropriate control action for the hydroponic environment, depending on the multiple input parameters collected, facilitating an effective management for farmers, thus improving their production.
Keywords
Hydroponics, Sigfox, Neural Networks, Ufox, Internet of Things, Smart Agriculture hidroponía, Sigfox, redes neuronales, Ufox, Internet de las cosas, agricultura inteligente
References
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[4] A. Medela, B. Cendón, L. González, R. Crespo, and I. Nevares, “Iot multiplatform networking to monitor and control wineries and vineyards,” in 2013 Future Network & Mobile Summit, 2013, pp. 1–10. [Online]. Available: https://bit.ly/3E6vwGx
[5] M. S. Farooq, S. Riaz, A. Abid, T. Umer, and Y. B. Zikria, “Role of iot technology in agriculture: A systematic literature review,” Electronics, vol. 9, no. 2, p. 319, 2020. [Online]. Available: https://doi.org/10.3390/electronics9020319
[6] L. García, L. Parra, J. M. Jimenez, J. Lloret, and P. Lorenz, “Iot-based smart irrigation systems: An overview on the recent trends on sensors and iot systems for irrigation in precision agriculture,” Sensors, vol. 20, no. 4, p. 1042, 2020. [Online]. Available: https://doi.org/10.3390/s20041042
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[8] M. Monica, B. Yeshika, G. S. Abhishek, H. A. Sanjay, and S. Dasiga, “Iot based control and automation of smart irrigation system: An automated irrigation system using sensors, gsm, bluetooth and cloud technology,” in 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE), 2017, pp. 601–607. [Online]. Available: https://bit.ly/3xlFgsv
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[10] M. R. Ramli, P. T. Daely, D.-S. Kim, and J. M. Lee, “Iot-based adaptive network mechanism for reliable smart farm system,” Computers and Electronics in Agriculture, vol. 170, p. 105287, 2020. [Online]. Available: https://doi.org/10.1016/j.compag.2020.105287
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[16] V. P. Kour and S. Arora, “Recent developments of the internet of things in agriculture: A survey,” IEEE Access, vol. 8, pp. 129 924–129 957, 2020. [Online]. Available:https://doi.org/10.1109/ACCESS.2020.3009298
[17] X. Shi, X. An, Q. Zhao, H. Liu, L. Xia, X. Sun, and Y. Guo, “State-of-the-art internet of things in protected agriculture,” Sensors, vol. 19, no. 8, p. 1833, 2019. [Online]. Available: https://doi.org/10.3390/s19081833
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[19] S. Santiteerakul, A. Sopadang, K. Yaibuathet Tippayawong, and K. Tamvimol, “The role of smart technology in sustainable agriculture: A case study of wangree plant factory,” Sustainability, vol. 12, no. 11, p. 4640, 2020. [Online]. Available: https://doi.org/10.3390/su12114640
[20] R. Pertierra Lazo and J. Quispe Gonzabay, “Análisis económico de lechugas hidropónicas bajo sistema raíz flotante en clima semiárido,” LA GRANJA: Revista de Ciencias de la Vida, vol. 31, no. 1, pp. 118–130, 2020. [Online]. Available: http://doi.org/10.17163/lgr.n31.2020.09
[21] D. D. Olatinwo, A. Abu-Mahfouz, and G. Hancke, “A survey on lpwan technologies in wban for remote health-care monitoring,” Sensors, vol. 19, no. 23, p. 5268, 2019. [Online]. Available: https://doi.org/10.3390/s19235268
[22] C. A. Ruiz and D. J. Matich, Redes Neuronales: Conceptos Básicos y Aplicaciones. Universidad Tecnológica Nacional, Facultad Regional Rosario, 2001. [Online]. Available: https://bit.ly/418PqdY
[23] C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006. [Online]. Available: https://bit.ly/3k6Ct3o
[24] I. Ullah, M. Fayaz, N. Naveed, and D. Kim, “Ann based learning to kalman filter algorithm for indoor environment prediction in smart greenhouse,” IEEE Access, vol. 8, pp. 159 371–159 388, 2020. [Online]. Available:https://doi.org/10.1109/ACCESS.2020.3016277
[25] D.-H. Jung, H. S. Kim, C. Jhin, H.-J. Kim, and S. H. Park, “Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse,” Computers and Electronics in Agriculture, vol. 173, p. 105402, 2020. [Online]. Available: https://doi.org/10.1016/j.compag.2020.105402
[26] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. John Wiley & Sons, 2012. [Online]. Available: https://bit.ly/3RZpY6g
[27] G. Saavedra, F. Corradini, A. Antúnez, S. Felmer, P. Estay, and P. Sepúlveda, Manual de producción de Lechuga. Instituto de Investigaciones Agropecuarias (INIA)., 2017. [Online]. Available: https://bit.ly/3RY6lf3
[28] E. L. Lehmann and G. Casella, Theory of Point Estimation. Springer Science & Business Media, 2006. [Online]. Available: https://bit.ly/3jX6XFb
[29] S. van Dongen and A. J. Enright, “Metric distances derived from cosine similarity and pearson and spearman correlations,” 2012. [Online]. Available: https://doi.org/10.48550/arXiv.1208.3145
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Nov 22, 2023
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