Desarrollo de un sistema para supervisión de pastoreo

Trabajo de InvestigaciónEn el trabajo mencionado se explica la metodología llevada a cabo para el diseño de un sistema de procesamiento de imágenes, aplicado al área de la agricultura de precisión, más precisamente al pastoreo rotacional. Esto con el fin de diseñar un sistema de control que facilite el desarrollo de dicha tarea, además de mejorar el proceso al automatizarlo.INTRODUCCIÓN 1. ANTECEDENTES 2. JUSTIFICACIÓN 3. OBJETIVOS 4. MARCO DE REFERENCIA 5. DESARROLLO DE PROPUESTA 6. ANÁLISIS DE RESULTADOS 7. CONCLUSIONES BIBLIOGRAFÍAPregradoIngeniero Electrónic

EXPO-AGRI: Smart Automatic Greenhouse Control

Predicting and controlling plant behavior in con- trolled environments is a growing requirement in precision agri- culture. In this context sensor networks and artificial intelligence methods represent key aspects for optimizing the processes of data acquisition, mathematical modeling and decision making. In this paper we present a general architecture for automatic greenhouse control. In particular, we focus on a preliminary model for predicting the risk of new infections of downy mildew of basil (Peronospora belbahrii) on sweet basil. The architecture has three main elements of innovation: new kinds of sensors are used to extract information about the state of the plants, model predictors are generated from this information by non-trivial processing methods, and informative predictors are automatically selected using regularization techniques

Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of Durum Wheat (Triticum turgidum L. var. durum) yield

In 2050, world population will reach a total of 9 billion inhabitants and their food demand have to be satisfied. Durum wheat (Triticum turgidum L. var. durum) is one of the most important food crop and its consumption is increasing worldwide. Productivity growth in agriculture and profitable returns are strongly influenced by investment in research and development, where Precision Agriculture (PA) represents an innovative way to manage farms by introducing the Information and Communication Technology (ICT) into the production process. It is known that farms activities produce large amounts of data. Today ICT allows, with electronic and software systems, to collect and transfer automatically these data, thus increasing yields and profits. In this direction significant data are processed from agricultural production, and retrieved to extract useful information important to increase the knowledge base. Data from multiple data sources can be processed by a Data Fusion (DF) approach able to combine multiple data sources into an unique database system. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. Data Fusion is synonym of Data Integration, Sensor Fusion, and Image Fusion. By means of Data Mining (DM) it is possible to extract useful information from data of the production processes thus providing new outputs concerning product quality and product “health status”. The following literature take into account the DF and DM techniques applied to Precision Agriculture (PA) and to cultivation inputs (water, nitrogen, etc.) management.  We report also last advances of DF and DM in modern agriculture and in precision durum wheat production

Survey of Impact of Technology on Effective Implementation of Precision Farming in India

The advancements in technology have made its impact on almost every field. India being an agricultural country, proper use of technology can greatly help in improving the standard of living of the farmers. With varying weather conditions, illiteracy of farmers and non-availability of timely assistance, the farmers of this country could not get the best out of their efforts. Precision farming focuses mainly on the aspects that can improve the efficiency based on the data collected from various sources viz. meteorology, sensors, GIS, GPS, etc. The information pertaining to farmland (e.g., soil moisture, soil pH, soil nitrogen) and agro-meteorology (e.g., temperature & humidity, solar radiation, wind speed, atmospheric CO2 concentration, rainfall, climate change and global warming) are used as input parameters to decide the varying requirements of the crop cultivation. Historical farm land data are used as a means to decide on the kind of actions to be taken under a specific scenario. This paper surveys the existing methods of precision farming and highlights the impact of technology in farming. An overview of different technologies used in precision farming around the world and their implications on the yield are discussed. The methods adopted towards managing different types of crops, the varying environmental conditions and the use of realtime data being collected through sensors are also analyzed. Also, the need for dynamic approaches to assist the farmers in taking context specific decisions has been highlighted

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

This work was supported by the contract OTRI-4408 between the University of Granada and the Royal Academy of Engineering of Spain financed by Ferrovial S.A. Eugenio Martinez Camara was supported by the Spanish Government fellowship programme Juan de la Cierva Incorporacion (IJC2018-036092-I).The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.University of Granada - Ferrovial S.A. OTRI-4408Royal Academy of Engineering of Spain - Ferrovial S.A. OTRI-4408Spanish Government fellowship programme Juan de la Cierva Incorporacion IJC2018-036092-

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

A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture

[EN] The use of precision agriculture is becoming more and more necessary to provide food for the world's growing population, as well as to reduce environmental impact and enhance the usage of limited natural resources. One of the main drawbacks that hinder the use of precision agriculture is the cost of technological immersion in the sector. For farmers, it is necessary to provide low-cost and robust systems as well as reliability. Toward this end, this paper presents a wireless sensor network of low-cost sensor nodes for soil moisture that can help farmers optimize the irrigation processes in precision agriculture. Each wireless node is composed of four soil moisture sensors that are able to measure the moisture at different depths. Each sensor is composed of two coils wound onto a plastic pipe. The sensor operation is based on mutual induction between coils that allow monitoring the percentage of water content in the soil. Several prototypes with different features have been tested. The prototype that has offered better results has a winding ratio of 1:2 with 15 and 30 spires working at 93 kHz. We also have developed a specific communication protocol to improve the performance of the whole system. Finally, the wireless network was tested, in a real, cultivated plot of citrus trees, in terms of coverage and received signal strength indicator (RSSI) to check losses due to vegetation.This work has been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3227 SMARTWATIR, 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 2017-2020" (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 2014-2020", GO TECNOGAR. This work has also been partially funded by the Universitat Politecnica de Valencia through the post-doctoral PAID-10-20 program.Lloret, J.; Sendra, S.; García-García, L.; Jimenez, JM. (2021). A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture. Sensors. 21(21):1-24. https://doi.org/10.3390/s21217243124212

Characterising the agriculture 4.0 landscape - Emerging trends, challenges and opportunities

ReviewInvestment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sectorinfo:eu-repo/semantics/publishedVersio

Monitoreo de cultivos con redes de sensores XBEE, arduino, y dispositivos de medición de suelos

El presente proyecto de grado consiste en el desarrollo de una aplicación web que permita a los usuarios un control remoto de sus plantaciones a través de la implementación de diversos dispositivos usados para brindar al usuario medidas históricas y en tiempo real de las variables más influyentes en todo el proceso de desarrollo de su cultivo. para lograr dichas medidas se usaran diferentes tipos de sensores los cuales arrojaran medidas de las variables cada determinado tiempo, que el usuario podrá establecer, estos sensores estarán conectados a un dispositivo arduino, el cual recibirá los datos y los enviara a través del módulo Xbee para él envió de los datos al computador que a través de un puerto serial recibirá los datos para posteriormente realizar una comunicación con la aplicación desarrollada en Django (Python) para almacenar las medidas en la base de datos, luego de esto se realizara una representación gráfica en intervalos de tiempo definidos por el usuario para poder observar el comportamiento de las variables a través del tiempo, generando también un sistema de alerta por correo electrónico en los casos en donde las medidas de las variables se salgan de los rangos permitidos por cada variable, también generara alertas cuando el nivel de la batería del dispositivo se encuentre bajo

Spatial-temporal analysis of subsurface water content and applications in Oklahoma: wastewater injection induced earthquakes and a multi sensor soil moisture product

Subsurface water is liquid water found below the ground surface, including soil water above the water table and ground water below the water table, but does not include water chemically bound to minerals or organic matter. Two important contents of subsurface water in Oklahoma have aroused the interest of more and more scientists: the wastewater injected into the ground during the oil and gas production and the surface soil moisture. This dissertation aims to develop contributions to two important topics for the sustainability of Oklahoma that are related to earthquakes and water resources: (1) the effects of deep underground waste-water injection on triggering regional seismicity and (2) the quantification of state-wide shallow-soil water content as a new tool for multiple applications in reservoir management, water resources, agriculture, natural hazards, and water management. The results of this study could help in setting sustainable limits for the oil and gas extraction industry in order to minimize the expected number and magnitude of induced quakes, thus avoiding future human and property losses. The results of this study also provide a new perspective for comparatively assessing multi-source soil moisture products, as well as a basis for objective data merging to capitalize on the strengths of multi-sensor multiplatform soil moisture products. Moreover, the new merged soil moisture product will be beneficial for multiple applications in water resources management, agriculture, and natural hazards