A New Conductivity Sensor for Monitoring the Fertigation in Smart Irrigation Systems

  • Javier Rocher
  • Daniel A. Basterrechea
  • Lorena Parra
  • Jaime LloretEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1006)


The incorrect fertilization of the crops can cause problems in the environment and extra costs. A solution is to perform fertigation controlling the amount of fertilizer in the water. In this paper, we test different combinations of coils for determining the amount of fertilizer in the water. A coil is powered by a sine wave of 3.3 peak-to-peak Volts for inducing another coil. These sensors will be included in a smart irrigation tube as a part of a smart irrigation system based on the Internet of Things (IoT). The aim of this system is to detect different sorts of problems that can cause incorrect fertilization, which affects the sustainability of agriculture. This system can be used in different scenarios where tubes are used to irrigate. We present the performed test to evaluate the suitability of the created prototypes. At first, we test with different dilutions of NaCl (table salt) and, after it, we performed tests with nitromagnesium (a fertilizer). We checked that at the same salinity the induction value changes if it is found in water with NaCl or nitromagnesium. Of all the tested prototypes it is concluded that the prototype P2 is the most optimal g/L because there is a difference in the induced voltage between 0 and 45 g/L of nitromagnesium of 3.79 V with a good correlation coefficient. In addition, the average error in the different samples tested in the verification test is 2.15%.


Coils Conductivity Fertigation Irrigation systems IoT 



This work has been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR by the “Ministerio de Educación, Cultura y Deporte”, through the “Ayudas para contratacion predoctoral de Formación del Profesorado Universitario FPU (Convocatoria 2016)”. Grant number FPU16/05540 and by 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.


  1. 1.
    Simons, J., Dannenman, M., Pena, R., Gessler, A., Renmenberg, H.: Nitrogen nutrition of beech forests in changing climate: importance of plant-soil-microbe water, carbon, and nitrogen interactions. Plant Soil 418(1–2), 89–114 (2017)CrossRefGoogle Scholar
  2. 2.
    Tsiafouli, M., et al.: Intensive agriculture reduces soil biodeversity across Europe. Glob. Chang. Biol. 21(2), 973–985 (2015)CrossRefGoogle Scholar
  3. 3.
    Wood, S.A., Almaraz, M., Bradford, M.A., McGuire, K.L., Naeem, S., Neill, C., Palm, C.A., Tully, K.L., Zhou, J.: Farm management, not soil microbial diversity, controls nutrient loss from smallholder tropica agriculture. Front. Microbiol. 6, 90 (2015)CrossRefGoogle Scholar
  4. 4.
    Lentz, R., Carter, D., Haye, S.: Changes in groundwater quality and agriculture in forty years on the Twin Falls irrigation tract in southern Idaho. Soil Water Conserv. 73(2), 107–119 (2018)CrossRefGoogle Scholar
  5. 5.
    Fernández, R., Fernández, J.A., López, B., López, J.A.: Aguas subterráneas y abastecimiento urbano, 1st edn. IGME, Castilla-La Mancha (2000)Google Scholar
  6. 6.
    Lawniczak, A.E., Zbierska, J., Nowak, B., Achtenberg, K., Grzeskowiak, A., Kanas, K.: Impact of agriculture and land use on nitrate contamination in groundwater and running waters in Central-West Poland. Environ. Monit. Assess 188, 172 (2016)CrossRefGoogle Scholar
  7. 7.
    García, L., Parra, L., Jimenez, J.M., Lloret, J., Lorenz, P.: Practical design of a WSN to monitor the crop and its irrigation system. Netw. Protoc. Algorithms 10(4), 35–52 (2018)CrossRefGoogle Scholar
  8. 8.
    Ramos, H., Gurriana, L., Postolache, O., Pereira, M., Girão, P.: Development and characterization of a conductivity cell for water quality monitoring. In: IEEE 3rd International Conference on Systems, Signals and Devices (SSD), Sousse (2005)Google Scholar
  9. 9.
    Parra, L., Sendra, S., Ortuño, V., Lloret, J.: Water conductivity measurements based on electromagnetic field. In: 1st International Conference on Computational Science and Engineering (CSE 2013), Valencia, pp. 139–144 (2013)Google Scholar
  10. 10.
    Gong, W., Mowlem, M., Kraft, M., Morgan, H.: Oceanographic sensor for in-situ temperature and conductivity monitoring. In: Oceans 2008-Mts/IEEE Kobe Techno-Ocean (OTO 2008), Kobe, pp. 1–6 (2008)Google Scholar
  11. 11.
    Parra, L., Sendra, S., Lloret, J., Rodriguez, J.: Low cost wireless sensor network for salinity monitoring in mangrove forests. In: SENSORS 2014, Valencia, pp. 126–129 (2014)Google Scholar
  12. 12.
    Parra, L., Sendra, S., Lloret, J., Bosch, I.: Development of a conductivity sensor for monitoring groundwater resources to optimize water management in smart city environments. Sensors 15(9), 20990–21015 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Javier Rocher
    • 1
  • Daniel A. Basterrechea
    • 1
  • Lorena Parra
    • 1
    • 2
  • Jaime Lloret
    • 1
    Email author
  1. 1.Instituto de Investigación para la Gestión Integrada de zonas CosterasUniversitat Politècnica de ValènciaGrao de Gandia, ValenciaSpain
  2. 2.IMIDRA. Finca “El Encin”Alcalá de Henares, MadridSpain

Personalised recommendations