A Rhizogenic Biostimulant Effect on Soil Fertility and Roots Growth of Turfgrass

[EN] The excessive use of chemical fertilizers can lead to severe environmental damages. In recent decades, the application of biostimulants to improve soil composition and stimulate plant growth has contributed significantly to environmental preservation. In this paper, we studied the effect of a rhizogenic biostimulant, obtained from fulvic acids, probiotics, and prebiotics, on the fertility of two types of soils, sandy and sandy loam soils, in which turfgrass was growing. Soil samples from plots treated with biostimulant and controls (untreated plots) were collected. The analyzed parameters from the soil include organic matter, microbial activity, soil chemical composition, catalase, dehydrogenase, and phosphatase enzyme activities. Moreover, root lengths was examined and compared in turfgrass species. The biostimulant application improved microbial activity, organic matter, and enzymatic activity in both types of soils. The soil calcium, potassium, magnesium, and phosphorus content increased with the biostimulant application, whereas pH and electrical conductivity decreased. The most relevant improvement was a 77% increase of calcium for sandy loam soil and 38% increase in potassium for sandy soil. Biostimulant application led to a significant increase in turf root length. This increase was greater for sandy soil than in sandy loam soil with an increment of 43% and 34% respectively, compared to control.This research was funded by AREA VERDE-MG projects, by project PDR18-XEROCESPED, funded under the PDR-CM 2014-2020 by the EU (European Agricultural Fund for Rural Development, EAFRD), Spanish Ministry of Agriculture, Fisheries and Food (MAPA) and Comunidad de Madrid regional government through IMIDRA. and by a post-doc grant by Conselleria de Educacion, Cultura y Deporte through "Subvenciones para la contratacion de personal investigador en fase postdoctoral", reference APOSTD/2019/04.Yousfi, S.; Marin, J.; Parra, L.; Lloret, J.; Mauri, PV. (2021). A Rhizogenic Biostimulant Effect on Soil Fertility and Roots Growth of Turfgrass. Agronomy. 11(3). https://doi.org/10.3390/agronomy11030573S11

RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C-3 and C-4 Turfgrass under Different Water Conditions

[EN] Grasslands have a natural capacity to decrease air pollution and a positive impact on human life. However, their maintenance requires adequate irrigation, which is difficult to apply in many regions where drought and high temperatures are frequent. Therefore, the selection of grass species more tolerant to a lack of irrigation is a fundamental criterion for green space planification. This study compared responses to deficit irrigation of different turfgrass mixtures: a C-4 turfgrass mixture, Cynodon dactylon-Brachypodium distachyon (A), a C-4 turfgrass mixture, Buchloe dactyloides-Brachypodium distachyon (B), and a standard C-3 mixture formed by Lolium perenne-Festuca arundinacea-Poa pratensis (C). Three different irrigation regimes were assayed, full irrigated to 100% (FI-100), deficit irrigated to 75% (DI-75), and deficit irrigated to 50% (DI-50) of container capacity. Biomass, normalized difference vegetation index (NDVI), green area (GA), and greener area (GGA) vegetation indices were measured. Irrigation significantly affected the NDVI, biomass, GA, and GGA. The most severe condition in terms of decreasing biomass and vegetation indices was DI-50. Both mixtures (A) and (B) exhibited higher biomass, NDVI, GA, and GGA than the standard under deficit irrigation. This study highlights the superiority of (A) mixture under deficit irrigation, which showed similar values of biomass and vegetation indices under full irrigated and deficit irrigated (DI-75) container capacities.This research was funded by AREA VERDE-MG projects and Projects GO-PDR18-XEROCESPED funded by the European Agricultural Fund for Rural Development (EAFRD) and IMIDRA.Marín, J.; Yousfi, S.; Mauri, PV.; Parra, L.; Lloret, J.; Masaguer, A. (2020). RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C-3 and C-4 Turfgrass under Different Water Conditions. Sustainability. 12(6):1-16. https://doi.org/10.3390/su1206216011612

Raman spectroscopy as probe of nanometer-scale strain variations in graphene

Confocal Raman spectroscopy is a versatile, non-invasive investigation tool and a major workhorse for graphene characterization. Here we show that the experimentally observed Raman 2D line width is a measure of nanometer-scale strain variations in graphene. By investigating the relation between the G and 2D line at high magnetic fields we find that the 2D line width contains valuable information on nanometer-scale flatness and lattice deformations of graphene, making it a good quantity for classifying the structural quality of graphene even at zero magnetic field.Comment: 7 pages, 4 figure

New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation

[EN] Water quality may be affected by aspects such as pollution from industries, agricultural fertilizers and pesticides, and waste produced by humans. This contamination can affect the produce of the fields irrigated by untreated water. Therefore, it is necessary to add a treatment process in irrigation systems. In this paper, an architecture, communication protocol, and a data analysis algorithm for a wastewater treatment system intended for irrigation are presented. Our system includes a smart group-based wireless sensor network that is able to detect high salinity levels and pollution stains, such as oil spills. When contamination is detected, the water is led into auxiliary canals that perform the biosorption process to treat the water and dump it back into the main canal. Simulations were performed to assess the amount of data stored on the secure digital (SD) card, the consumed bandwidth, and the energy consumption of our proposal. The results show the system has a low bandwidth consumption with a maximum of 2.58 kbps for the setting of two daily data transmissions of the node in the last auxiliary canal. Furthermore, it can sustain the energy consumption in adverse conditions, where the node with the highest energy consumption reaches the lowest energy value of 12,320 mW/h.This research was partially funded by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR. This work has also been partially funded by the Universitat Politecnica de Valencia through the post-doctoral PAID-10-20 program and by Conselleria de Educacion, Cultura y Deporte with the Subvenciones para la contratacion de personal investigador en fase postdoctoral, grant number APOSTD/2019/04.Jimenez, JM.; Parra-Boronat, L.; García, L.; Lloret, J.; Mauri, PV.; Lorenz, P. (2021). New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation. Applied Sciences. 11(8):1-21. https://doi.org/10.3390/app11083648S12111

Low-cost Soil Moisture Sensors Based on Inductive Coils Tested on Different Sorts of Soils

[EN] The use of precision agriculture and the Internet of Things has improved the efficiency of many cultures. Nevertheless, there are a few low-cost options to monitor soil moisture. Moreover, those options depend on the specific characteristics of the soil. In this paper, we attempt to find a sensor, based on mutual inductance, that could be used for more than one sort of soil. We study three prototypes, one of them with casing. The sensors are powered with a voltage of 10 peak to peak volts. One of the soils has a high content of organic matter and sand while the other is rich in sand and silt. The best prototype for the soil with high levels of organic matter has 10 turns on the powered coil and 5 on the induced coil. The best frequency for this sensor is 1340 kHz. For the soil with a significant quantity of silt, the best prototype has 80 turns on the powered coil and 40 on the induced coil. The frequency at which this sensor works best is 229 kHz, which happens to be its peak frequency. With those characteristics regressions lines with R2 values higher than 0.75 can be modeledThis 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, M.; Parra-Boronat, L.; Lloret, J.; Mauri, PV.; Llinares Palacios, JV. (2019). Low-cost Soil Moisture Sensors Based on Inductive Coils Tested on Different Sorts of Soils. IEEE. 616-622. https://doi.org/10.1109/IOTSMS48152.2019.8939258S61662

Remote Sensing: Useful Approach for Crop Nitrogen Management and Sustainable Agriculture

Soil fertility is among the most important criteria that affect crop yield and quality. Nitrogen stress due to the low soil fertility and the lack of nitrogen availability is a major factor limiting the crop productivity in arid and semiarid environments, where fertilization is not optimized in terms of timing and quantity. Managing nitrogen fertilization is one of the most important criteria in the precision agriculture, which helps to improve crop production, environment conditions, and farmer’s economy. It is very important to apply N fertilizers with efficient methods allowing to the nutrient use efficiency and avoiding nitrogen losses and environment contamination. Nowadays, remote sensing methods using spectral and thermal approaches have been proposed as potential indicators to rapid identification of crop nitrogen status by providing information about vegetation canopy properties across large areas. The use of remote sensing methods to schedule nitrogen fertilization can help farmers to practice a more sustainable agriculture, minimizing risks of losing the harvest by providing an adequate rate of nitrogen when the crops’ needs and at a specific location

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-

DronAway: A Proposal on the Use of Remote Sensing Drones as Mobile Gateway for WSN in Precision Agriculture

[EN] The increase in the world population has led to new needs for food. Precision Agriculture (PA) is one of the focuses of these policies to optimize the crops and facilitate crop management using technology. Drones have been gaining popularity in PA to perform remote sensing activities such as photo and video capture as well as other activities such as fertilization or scaring animals. These drones could be used as a mobile gateway as well, benefiting from its already designed flight plan. In this paper, we evaluate the adequacy of remote sensing drones to perform gateway functionalities, providing a guide for choosing the best drone parameters for successful WiFi data transmission between sensor nodes and the gateway in PA systems for crop monitoring and management. The novelty of this paper compared with existing mobile gateway proposals is that we are going to test the performance of the drone that is acting as a remote sensing tool to carry a low-cost gateway node to gather the data from the nodes deployed on the field. Taking this in mind, simulations of different scenarios were performed to determine if the data can be transmitted correctly or not considering different flying parameters such as speed (from 1 to 20 m/s) and flying height (from 4 to 104 m) and wireless sensor network parameters such as node density (1 node each 60 m(2) to 1 node each 5000 m(2)) and antenna coverage (25 to 200 m). We have calculated the time that each node remains with connectivity and the time required to send the data to estimate if the connection will be bad, good, or optimal. Results point out that for the maximum node density, there is only one combination that offers good connectivity (lowest velocity, the flying height of 24 m, and antenna with 25 m of coverage). For the other node densities, several combinations of flying height and antenna coverage allows good and optimal connectivity.This work is partially founded 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, by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by Conselleria de Educacion, Cultura y Deporte with the Subvenciones para la contratacion de personal investigador en fase postdoctoral, grant number APOSTD/2019/04.García, L.; Parra-Boronat, L.; Jimenez, JM.; Lloret, J.; Mauri, PV.; Lorenz, P. (2020). DronAway: A Proposal on the Use of Remote Sensing Drones as Mobile Gateway for WSN in Precision Agriculture. Applied Sciences. 10(19):1-23. https://doi.org/10.3390/app10196668S1231019Agriculture and Rural Development Agriculture and Rural Development https://ec.europa.eu/agriculture/cap-post-2013/Kropff, M. J., Wallinga, J., & Lotz, L. A. P. (2007). Modelling for Precision Weed Management. Ciba Foundation Symposium 210 - Precision Agriculture: Spatial and Temporal Variability of Environmental Quality, 182-207. doi:10.1002/9780470515419.ch12Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 22-36. doi:10.1016/j.isprsjprs.2015.10.004Pajares, G. (2015). Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81(4), 281-330. doi:10.14358/pers.81.4.281Maes, W. H., & Steppe, K. (2019). Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends in Plant Science, 24(2), 152-164. doi:10.1016/j.tplants.2018.11.007Psirofonia, P., Samaritakis, V., Eliopoulos, P., & Potamitis, I. (2017). Use of Unmanned Aerial Vehicles for Agricultural Applications with Emphasis on Crop Protection: Three Novel Case - studies. International Journal of Agricultural Science and Technology, 5(1), 30-39. doi:10.12783/ijast.2017.0501.03Agriculture Drones Market by Offering (Hardware and Software & Services), Application (Precision Farming, Livestock Monitoring, Precision Fish Farming, and Smart Greenhouse), Component, and Geography—Global Forecast to 2024 https://www.marketsandmarkets.com/Market-Reports/agriculture-drones-market-23709764.html?gclid=CjwKCAiA-P7xBRAvEiwAow-VaRPLzQ4x9YHOwUyC4e-PBfJvjpkB4Bqx9WWIt6S-lM0FsKvUcbqLdxoC_VcQAvD_BwECunliffe, A. M., Brazier, R. E., & Anderson, K. (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment, 183, 129-143. doi:10.1016/j.rse.2016.05.019Zhang, J., Hu, J., Lian, J., Fan, Z., Ouyang, X., & Ye, W. (2016). Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring. Biological Conservation, 198, 60-69. doi:10.1016/j.biocon.2016.03.027Urbahs, A., & Jonaite, I. (2013). FEATURES OF THE USE OF UNMANNED AERIAL VEHICLES FOR AGRICULTURE APPLICATIONS. Aviation, 17(4), 170-175. doi:10.3846/16487788.2013.861224Raeva, P. L., Šedina, J., & Dlesk, A. (2018). Monitoring of crop fields using multispectral and thermal imagery from UAV. European Journal of Remote Sensing, 52(sup1), 192-201. doi:10.1080/22797254.2018.1527661Stehr, N. J. (2015). Drones: The Newest Technology for Precision Agriculture. Natural Sciences Education, 44(1), 89-91. doi:10.4195/nse2015.04.0772Kurkute, S. R. (2018). Drones for Smart Agriculture: A Technical Report. International Journal for Research in Applied Science and Engineering Technology, 6(4), 341-346. doi:10.22214/ijraset.2018.4061Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507-518. doi:10.1080/09720510.2017.1395171Valente, J., Sanz, D., Barrientos, A., Cerro, J. del, Ribeiro, Á., & Rossi, C. (2011). An Air-Ground Wireless Sensor Network for Crop Monitoring. Sensors, 11(6), 6088-6108. doi:10.3390/s110606088Hunt, E. R., & Daughtry, C. S. T. (2017). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing, 39(15-16), 5345-5376. doi:10.1080/01431161.2017.1410300Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A Review on UAV-Based Applications for Precision Agriculture. Information, 10(11), 349. doi:10.3390/info10110349Daponte, P., De Vito, L., Glielmo, L., Iannelli, L., Liuzza, D., Picariello, F., & Silano, G. (2019). A review on the use of drones for precision agriculture. IOP Conference Series: Earth and Environmental Science, 275, 012022. doi:10.1088/1755-1315/275/1/012022Boehm, F., & Schulte, A. (2013). Air to ground sensor data distribution using IEEE802.11N Wi-Fi network. 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC). doi:10.1109/dasc.2013.6712581Stek, T. D. (2016). Drones over Mediterranean landscapes. The potential of small UAV’s (drones) for site detection and heritage management in archaeological survey projects: A case study from Le Pianelle in the Tappino Valley, Molise (Italy). Journal of Cultural Heritage, 22, 1066-1071. doi:10.1016/j.culher.2016.06.006Marín, J., Parra, L., Rocher, J., Sendra, S., Lloret, J., Mauri, P. V., & Masaguer, A. (2018). Urban Lawn Monitoring in Smart City Environments. Journal of Sensors, 2018, 1-16. doi:10.1155/2018/8743179Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66-84. doi:10.1016/j.compag.2015.08.011Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007Aqeel-ur-Rehman, Abbasi, A. Z., Islam, N., & Shaikh, Z. A. (2014). A review of wireless sensors and networks’ applications in agriculture. Computer Standards & Interfaces, 36(2), 263-270. doi:10.1016/j.csi.2011.03.004Ruiz-Garcia, L., Lunadei, L., Barreiro, P., & Robla, I. (2009). A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends. Sensors, 9(6), 4728-4750. doi:10.3390/s90604728Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297-307. doi:10.1016/j.jclepro.2014.04.036Yu, X., Wu, P., Han, W., & Zhang, Z. (2013). A survey on wireless sensor network infrastructure for agriculture. Computer Standards & Interfaces, 35(1), 59-64. doi:10.1016/j.csi.2012.05.001Chaudhary, D. D., Nayse, S. P., & Waghmare, L. M. (2011). Application of Wireless Sensor Networks for Greenhouse Parameter Control in Precision Agriculture. International Journal of Wireless & Mobile Networks, 3(1), 140-149. doi:10.5121/ijwmn.2011.3113Díaz, S. E., Pérez, J. C., Mateos, A. C., Marinescu, M.-C., & Guerra, B. B. (2011). A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks. Computers and Electronics in Agriculture, 76(2), 252-265. doi:10.1016/j.compag.2011.02.004Zhu, Y., Song, J., & Dong, F. (2011). Applications of wireless sensor network in the agriculture environment monitoring. Procedia Engineering, 16, 608-614. doi:10.1016/j.proeng.2011.08.1131Keshtgari, M., & Deljoo, A. (2012). A Wireless Sensor Network Solution for Precision Agriculture Based on Zigbee Technology. Wireless Sensor Network, 04(01), 25-30. doi:10.4236/wsn.2012.41004Hwang, J., Shin, C., & Yoe, H. (2010). Study on an Agricultural Environment Monitoring Server System using Wireless Sensor Networks. Sensors, 10(12), 11189-11211. doi:10.3390/s101211189Garcia-Sanchez, A.-J., Garcia-Sanchez, F., & Garcia-Haro, J. (2011). Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Computers and Electronics in Agriculture, 75(2), 288-303. doi:10.1016/j.compag.2010.12.005García, L., Parra, L., Jimenez, J. M., Lloret, J., & Lorenz, P. (2019). Practical Design of a WSN to Monitor the Crop and its Irrigation System. Network Protocols and Algorithms, 10(4), 35. doi:10.5296/npa.v10i4.14147Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., & Dragana, C. (2020). Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture. Sensors, 20(3), 817. doi:10.3390/s20030817Specifications of the WEMOS MINI DI https://docs.wemos.cc/en/latest/d1/d1_mini.htmlSpecifications of the Node MCU https://joy-it.net/en/products/SBC-NodeMCU-ESP32Specifications of the Arduino Mega https://store.arduino.cc/arduino-mega-2560-rev3Specifications of the Arduino UNO https://store.arduino.cc/arduino-uno-rev3Specifications of the Raspberry Pi Model B+ https://www.raspberrypi-spy.co.uk/2018/03/introducing-raspberry-pi-3-b-plus-computer/Zorbas, D., Di Puglia Pugliese, L., Razafindralambo, T., & Guerriero, F. (2016). Optimal drone placement and cost-efficient target coverage. Journal of Network and Computer Applications, 75, 16-31. doi:10.1016/j.jnca.2016.08.009Parra, L., Rocher, J., García, L., Lloret, J., Tomás, J., Romero, O., … Roig, B. (2018). Design of a WSN for smart irrigation in citrus plots with fault-tolerance and energy-saving algorithms. Network Protocols and Algorithms, 10(2), 95. doi:10.5296/npa.v10i2.1320

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

The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

This research was partially funded by project PDR18-XEROCESPED, under the PDR-CM 2014-2020, by the EU (European Agricultural Fund for Rural Development, EAFRD), Ministerio de Agricultura, Pesca y Alimentacion (MAPA) and Comunidad de Madrid regional government through IMIDRA, by the Programa Estatal de I+D+i Orientada a los Retos de la Sociedad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 20172020 (Project code: PID2020-114467RR-C33), and by Proyectos de innovacion de interes general por grupos operativos de la Asociacion Europea para la Innovacion en materia de productividad y sostenibilidad agricolas (AEI-Agri) in the framework Programa Nacional de Desarrollo Rural 20142020, GO TECNOGAR, and by Conselleria de Educacion, Cultura y Deporte, through Subvenciones para la contratacion de personal investigador en fase postdoctoral APOSTD/2019/04.Mauri, PV.; Parra, L.; Mostaza-Colado, D.; García-García, L.; Lloret, J.; Marin, JF. (2021). The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course. Applied Sciences. 11(24):1-17. https://doi.org/10.3390/app112411769117112