Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
Date
2019-05-14
2019-05-14
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Abstract
The production of tomatoes in greenhouses, in addition to its relevance in nutrition and health, is an activity of the agroindustry with high economic importance in Spain, the first exporter in Europe of this vegetable. The technological updating with precision agriculture, implemented in order to ensure adequate production, leads to a deployment planning of wireless sensors with limited coverage by the attenuation of radio waves in the presence of vegetation. The well-known propagation models FSPL (Free-Space Path Loss), two-ray, COST235, Weissberger, ITU-R (International Telecommunications Union—Radiocommunication Sector), FITU-R (Fitted ITU-R), offer values with an error percentage higher than 30% in the 2.4 GHz band in relation to those measured in field tests. As a substantial improvement, we have developed optimized propagation models, with an error estimate of less than 9% in the worst-case scenario for the later benefit of farmers, consumers and the economic chain in the production of tomatoes.
Referencias:
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1. Razafimandimby, C.; Loscrí, V.; Vegni, A.M.; Neri, A. Efficient Bayesian communication approach for smart agriculture applications. In Proceedings of the 2017 IEEE Vehicular Technology Conference, Toronto, ON, Canada, 24–27 September 2017; pp. 1–5. [CrossRef] 2. Caicedo-Ortiz, J.G.; De-la-Hoz-Franco, E.; Morales Ortega, R.; Piñeres-Espitia, G.; Combita-Niño, H.; Estévez, F.; Cama-Pinto, A. Monitoring system for agronomic variables based in WSN technology on cassava crops. Comput. Electron. Agric. 2018, 145, 275–281. [CrossRef] 3. Tzounis, A.; Katsoulas, N.; Bartzanas, T.; Kittas, C. Internet of Things in agriculture, recent advances and future challenges. Biosyst. Eng. 2017, 164, 31–48. [CrossRef] 4. Sabri, N.; Mohammed, S.S.; Fouad, S.; Syed, A.A.; Al-Dhief, F.T.; Raheemah, A. Investigation of Empirical Wave Propagation Models in Precision Agriculture. MATEC Web Conf. 2018, 150, 06020. [CrossRef] 5. Correia, F.P.; De Alencar, M.S.; Lopes, W.T.A.; De Assis, M.S.; Leal, B.G. Propagation analysis for wireless sensor networks applied to viticulture. Int. J. Antennas Propag. 2017, 2017, 7903839. [CrossRef] 6. Yoshimura, R.; Hara, M.; Nishimura, T.; Yamada, C.; Shimasaki, H.; Kado, Y.; Ichida, M. Effect of vegetation on radio wave propagation in 920-MHz and 2.4-GHz bands. In Proceedings of the Asia-Pacific Microwave Conference (APMC), New Delhi, India, 5–9 December 2016. [CrossRef] 7. Correia, F.P.; Alencar, M.S.; Carvalho, F.B.S.; Lopes, W.T.A.; Leal, B.G. Propagation analysis in precision agriculture environment using XBee devices. In Proceedings of the SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference, Rio de Janeiro, Brazil, 4–7 August 2013. [CrossRef] 8. Li, J.; Shen, C. Energy conservative Wireless Sensor Networks for black pepper monitoring in tropical area. In Proceedings of the IEEE Global High Tech Congress on Electronics (GHTCE), Shenzhen, China, 17–19 November 2013; pp. 159–164. [CrossRef] 9. Montoya, F.G.; Gomez, J.; Manzano-Agugliaro, F.; Cama, A.; García-Cruz, A.; De La Cruz, J.L. 6LoWSoft: A software suite for the design of outdoor environmental measurements. J. Food Agric. Environ. 2013, 11, 2584–2586. 10. Holvoet, K.; Sampers, I.; Seynnaeve, M.; Jacxsens, L.; Uyttendaele, M. Agricultural and management practices and bacterial contamination in greenhouse versus open field lettuce production. Int. J. Environ. Res. Public Health 2015, 12, 32–63. [CrossRef] [PubMed] 11. Sabri, N.; Aljunid, S.A.; Salim, M.S.; Kamaruddin, R.; Ahmad, R.B.; Malek, M.F. Path loss analysis of WSN wave propagation in vegetation. J. Phys. Conf. Ser. 2013, 423, 012063. [CrossRef] 12. Paul, B.S.; Rimer, S. A foliage scatter model to determine topology of wireless sensor network. In Proceedings of the International Conference on Radar, Communication and Computing (ICRCC), Tiruvannamalai, India, 21–22 December 2012; pp. 324–328. [CrossRef] 13. Liu, H.; Meng, Z.; Wang, M. A wireless sensor network for cropland environmental monitoring. In Proceedings of the International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC), Wuhan, China, 25–26 April 2009; Volume 1, pp. 65–68. [CrossRef] 14. Piñeres-Espitia, G.; Cama-Pinto, A.; De La Rosa Morrón, D.; Estevez, F.; Cama-Pinto, D. Design of a low cost weather station for detecting environmental changes. Espacios 2017, 38, 13. 15. Sánchez, J.A.; Reca, J.; Martínez, J. Water productivity in a mediterranean semi-arid greenhouse district. Water Resour. Manag. 2015, 29, 5395–5411. [CrossRef] 16. De Pablo-Valenciano, J.; Giacinti-Battistuzzi, M.A.; Tassile, V.; García-Azcárate, T. Changes in the business model for Spanish fresh tomato trade. Span. J. Agric. Res. 2017, 15, e0101. [CrossRef] 17. Marín, P.; Valera, D.L.; Molina-Aiz, F.D.; López, A.; Belmonte, L.J.; Moreno, M.A. Influence of different heating systems on the development, production and quality of a tomato crop. ITEA Inf. Tec. Econ. Agrar. 2016, 112, 375–391. [CrossRef] 18. Vougioukas, S.; Anastassiu, H.T.; Regen, C.; Zude, M. Influence of foliage on radio path losses (PLs) for Wireless Sensor Network (WSN) planning in orchards. Biosyst. Eng. 2013, 114, 454–465. [CrossRef] 19. Raheemah, A.; Sabri, N.; Salim, M.S.; Ehkan, P.; Ahmad, R.B. New empirical path loss model for wireless sensor networks in mango greenhouses. Comput. Electron. Agric. 2016, 127, 553–560. [CrossRef] 20. Mancuso, M.; Bustaffa, F. A Wireless Sensors Network for monitoring environmental variables in a tomato greenhouse. In Proceedings of the IEEE International Workshop on Factory Communication Systems (WFCS), Torino, Italy, 28–30 June 2006; pp. 107–110. 21. Erazo-Rodas, M.; Sandoval-Moreno, M.; Muñoz-Romero, S.; Huerta, M.; Rivas-Lalaleo, D.; Naranjo, C.; Rojo-álvarez, J.L. Multiparametric monitoring in equatorian tomato greenhouses (I): Wireless sensor network benchmarking. Sensors 2018, 18, 2555. [CrossRef] [PubMed] 22. 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