Scatternet Formation Protocol for Environmental Monitoring in a Smart Garden

[EN] The monitoring of different parameters in the smart garden environment requires thousands of nodes and actuators. They form a multi-hop communication network. The scatternets formed with Bluetooth protocol is a communication solution. However, there is no current algorithm that considers the different capabilities of the devices (sensors or actuators) and assigns a role according to these capabilities. In this paper, we present a network topology formation algorithm for role assignment and connection establishment which considers the capabilities of the devices and use slave-slave Bridge to communicate the piconets. We design the algorithms needed for this protocol and test it. We have simulated the algorithms in order to evaluate the time needed for role assignment and to establish the first connections of the piconet. The results include different scenarios composed by one or two masters and one to seven slaves. In addition, we evaluate the established connections in piconets and bridges in a real case of the smart garden sensor network. Finally, we present the changes in the piconet connections after the deployment of two nodes in an existing network.This work is partially found by the European Union with the “Fondo Europeo Agrícola de Desarrollo Rural (FEADER) – Europa invierte en zonas rurales”, the MAPAMA, and Comunidad de Madrid with the IMIDRA, under the mark of the PDR-CM 2014-2020” project number PDR18-XEROCESPED. This work has been partially supported by the "Ministerio de Economía y Competitividad" in the "Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.Parra-Boronat, L.; Marín, J.; Mauri Ablanque, PV.; Lloret, J.; Torices, V.; Massager, A. (2018). Scatternet Formation Protocol for Environmental Monitoring in a Smart Garden. Network Protocols and Algorithms. 10(3):63-84. https://doi.org/10.5296/npa.v10i3.14122S638410

Comparison of Single Image Processing Techniques and Their Combination for Detection of Weed in Lawns

[EN] The detection of weeds in lawns is important due to the different negative effects of its presence. Those effects include a lack of uniformity and competition for the resources. If the weeds are detected early the phytosanitary treatment, which includes the use of toxic substances, will be more effective and will be applied to a smaller surface. In this paper, we propose the use of image processing techniques for weed detection in urban lawns. The proposed methodology is based on simple techniques in order to ensure that they can be applied in-situ. We propose two techniques, one of them is based on the mathematical combination of the red, green and blue bands of an image. In this case, two mathematical operations are proposed to detect the presence of weeds, according to the different colorations of plants. On the other hand, we proposed the use of edge detection techniques to differentiate the surface covered by grass from the surface covered by weeds. In this case, we compared 12 different filters and their combinations. The best results were obtained with the Laplacian filter. Moreover, we proposed to use pre-processing and post-processing operations to remove the soil and to aggregate the data with the aim of reducing the number of false positives. Finally, we compared both methods and their combination. Our results show that both methods are promising, and its combination reduces the number of false positives (0 false positives in the 4 evaluated images) ensuring the detection of all weeds.This work is partially found by the Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by the European Union with the "Fondo Europeo Agrícola de Desarrollo Rural (FEADER) - Europa invierte en zonas rurales", the MAPAMA, and Comunidad de Madrid with the IMIDRA, under the mark of the PDR-CM 2014-2020 project number PDR18-XEROCESPED.Parra-Boronat, L.; Parra-Boronat, M.; Torices, V.; Marín, J.; Mauri, PV.; Lloret, J. (2019). Comparison of Single Image Processing Techniques and Their Combination for Detection of Weed in Lawns. International Journal On Advances in Intelligent Systems. 12(3-4):177-190. http://hdl.handle.net/10251/158241S177190123-

New Sensor Based on Magnetic Fields for Monitoring the Concentration of Organic Fertilisers in Fertigation Systems

[EN] In this paper, we test three prototypes with different characteristics for controlling the quantity of organic fertiliser in the agricultural irrigation system. We use 0.4 mm of copper diameter, distributing in different layers, maintaining the relation of 40 spires for powered coil and 80 for the induced coil. Moreover, we develop sensors with 8, 4, and 2 layers of copper. The coils are powered by a sine wave of 3.3 V peak to peak, and the other part is induced. To verify the functioning of this sensor, we perform several simulations with COMSOL Multiphysics to verify the magnetic field created around the powered coil, as well as the electric field, followed by a series of tests, using six samples between the 0 g/L and 20 g/L of organic fertiliser, and measure their conductivity. First, we find the working frequency doing a sweep for each prototype and four configurations. In this case, for all samples, making a sweep between 10 kHz and 300 kHz. We obtained that in prototype 1 (P1) (coil with 8 layers) the working frequency is around 100 kHz, in P2 (coil with 4 layers) around 110 kHz, and for P3 (coil with 2 layers) around 140 kHz. Then, we calibrate the prototypes measuring the six samples at four different configurations for each sensor to evaluate the possible variances. Likewise, the measures were taken in triplicate to reduce the possible errors. The obtained results show that the maximum difference of induced voltage between the lowest and the highest concentration is for the P2/configuration 4 with 1.84 V. Likewise, we have obtained an optimum correlation of 0.997. Then, we use the other three samples to verify the optimum functioning of the obtained calibrates. Moreover, the ANOVA simple procedure is applied to the data of all prototypes, in the working frequency of each configuration, to verify the significant difference between the values. The obtained results indicate that there is a significate difference between the average of concentration (g/L) and the induced voltage, and another with a level of 5% of significance. Finally, we compare all of the tested prototypes and configurations, and have determined that prototype three with configuration 1 is the best device to be used as a fertiliser sensor in water.This work is partially funded by the Conselleria de Educacion, Cultura y Deporte with the Subvenciones para la contratacion de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, by the European Union, through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by the European Union with the "Fondo Europeo Agricola de Desarrollo Rural (FEADER)-Europa invierte en zonas rurales", the MAPAMA, and Comunidad de Madrid with the IMIDRA, under the mark of the PDR-CM 2014-2020" project number PDR18-XEROCESPED.Basterrechea-Chertudi, DA.; Parra-Boronat, L.; Botella-Campos, M.; Lloret, J.; Mauri, PV. (2020). New Sensor Based on Magnetic Fields for Monitoring the Concentration of Organic Fertilisers in Fertigation Systems. Applied Sciences. 10(20):1-28. https://doi.org/10.3390/app102072221281020World Agriculture 2030: Main Findingshttp://www.fao.org/english/newsroom/news/2002/7833-en.htmlGamarra, C., Díaz Lezcano, M. I., Vera de Ortíz, M., Galeano, M. D. P., & Cabrera Cardús, A. J. N. (2018). Relación carbono-nitrógeno en suelos de sistemas silvopastoriles del Chaco paraguayo. Revista Mexicana de Ciencias Forestales, 9(46). doi:10.29298/rmcf.v9i46.134Too Much Organic Matterhttps://www.mofga.org/Publications/The-Maine-Organic-Farmer-Gardener/Fall-2009/Organic-MatterNasir Khan, M. (2018). OBSOLETE: Fertilizers and Their Contaminants in Soils, Surface and Groundwater. Reference Module in Earth Systems and Environmental Sciences. doi:10.1016/b978-0-12-409548-9.09888-2Gebbers, R., & Adamchuk, V. I. (2010). Precision Agriculture and Food Security. Science, 327(5967), 828-831. doi:10.1126/science.1183899Feng, C., Lü, S., Gao, C., Wang, X., Xu, X., Bai, X., … Wu, L. (2015). «Smart» Fertilizer with Temperature- and pH-Responsive Behavior via Surface-Initiated Polymerization for Controlled Release of Nutrients. ACS Sustainable Chemistry & Engineering, 3(12), 3157-3166. doi:10.1021/acssuschemeng.5b01384Ni, B., Liu, M., Lü, S., Xie, L., & Wang, Y. (2011). Environmentally Friendly Slow-Release Nitrogen Fertilizer. Journal of Agricultural and Food Chemistry, 59(18), 10169-10175. doi:10.1021/jf202131zSouza, C. F., Faez, R., Bacalhau, F. B., Bacarin, M. F., & Pereira, T. S. (2017). IN SITU MONITORING OF A CONTROLLED RELEASE OF FERTILIZERS IN LETTUCE CROP. Engenharia Agrícola, 37(4), 656-664. doi:10.1590/1809-4430-eng.agric.v37n4p656-664/2017Merten, G. H., Capel, P. D., & Minella, J. P. G. (2013). Effects of suspended sediment concentration and grain size on three optical turbidity sensors. Journal of Soils and Sediments, 14(7), 1235-1241. doi:10.1007/s11368-013-0813-0Comsol Multiphysicshttps://www.comsol.com/Kleinberg, R. L., Chew, W. C., & Griffin, D. D. (1989). Noncontacting electrical conductivity sensor for remote, hostile environments. IEEE Transactions on Instrumentation and Measurement, 38(1), 22-26. doi:10.1109/19.19992Parra, L., Marín, J., Mauri, P. V., Lloret, J., Torices, V., & Massager, A. (2019). Scatternet Formation Protocol for Environmental Monitoring in a Smart Garden. Network Protocols and Algorithms, 10(3), 63. doi:10.5296/npa.v10i3.14122Wood, L. T., Rottmann, R. M., & Barrera, R. (2004). Faraday’s law, Lenz’s law, and conservation of energy. American Journal of Physics, 72(3), 376-380. doi:10.1119/1.1646131Parra, L., Sendra, S., Lloret, J., & Bosch, I. (2015). Development of a Conductivity Sensor for Monitoring Groundwater Resources to Optimize Water Management in Smart City Environments. Sensors, 15(9), 20990-21015. doi:10.3390/s150920990Karagianni, E. A. (2015). ELECTROMAGNETIC WAVES UNDER SEA: BOW-TIE ANTENNAS DESIGN FOR WI-FI UNDERWATER COMMUNICATIONS. Progress In Electromagnetics Research M, 41, 189-198. doi:10.2528/pierm15012106https://www.tek.com/signal-generator/afg1022https://www.tek.com/oscilloscope/tbs1104http://www.crisoninstruments.com/es/laboratorio/conductimetro/desobremesa/ec-metro-basic-30https://www.leroymerlin.es/fp/19468554/fertilizante-para-citricos-geolia-uso-ecologico-1lhttps://statgraphics.net/descargas-centurion-xvii/Matsoukis, A., Kamoutsis, A., & Chronopoulou-Sereli, A. (2018). A Note on the Flowering of Ajuga orientalis L. in Relation to Air Temperature in Mount Aenos (Cephalonia, Greece). Current Agriculture Research Journal, 6(3), 261-267. doi:10.12944/carj.6.3.0