Neural Networks-Based Models for Greenhouse Climate Control

[Abstract] Greenhouses are multivariable and nonlinear systems with high degree of complexity, so it is hard to build models that represent the whole dynamics of the system. This paper presents models of greenhouse climate based on neural networks. The models predict inside air temperature and relative humidity in the greenhouse as a function of the variables used as input for the network, as outside temperature, relative humidity, solar radiation, etc., and the actuators state signals, as window opening and others. Data sets used for modelling have been measured with real red pepper plants inside the greenhouse. The developed models are described and the achieved results are reported.[Resumen] Los invernaderos son sistemas multivariables y no lineales con un alto grado de complejidad, por lo que es difícil construir modelos que representen toda la dinámica del sistema. Este artículo presenta modelos de clima de invernadero basados en redes neuronales. Los modelos predicen la temperatura del aire interior y la humedad relativa en el invernadero en función de las variables utilizadas como entrada para la red, como la temperatura exterior, la humedad relativa, la radiación solar, etc., y las señales de estado de los actuadores, como la apertura de la ventana y otros. Los conjuntos de datos utilizados para el modelado se han medido con plantas reales de pimiento rojo dentro del invernadero. Se describen los modelos desarrollados y se reportan los resultados obtenidosJunta de Extremadura; GR1815

Architecture and Applications of IoT Devices in Socially Relevant Fields

Number of IoT enabled devices are being tried and introduced every year and there is a healthy competition among researched and businesses to capitalize the space created by IoT, as these devices have a great market potential. Depending on the type of task involved and sensitive nature of data that the device handles, various IoT architectures, communication protocols and components are chosen and their performance is evaluated. This paper reviews such IoT enabled devices based on their architecture, communication protocols and functions in few key socially relevant fields like health care, farming, firefighting, women/individual safety/call for help/harm alert, home surveillance and mapping as these fields involve majority of the general public. It can be seen, to one's amazement, that already significant number of devices are being reported on these fields and their performance is promising. This paper also outlines the challenges involved in each of these fields that require solutions to make these devices reliableComment: 1

Artificial Intelligence: The Future of Sustainable Agriculture? A Research Agenda

Global warming and the increasing food demand are problems of the current generation and require a change towards sustainable agriculture. In recent years, research in the field of artificial intelligence has made considerable progress. Thus, the use of artificial intelligence in agriculture can be a promising solution to ensure sufficient food supply on a global scale. To investigate the state-of-the-art in the use of artificial intelligence-based systems in agriculture, we provide a structured literature review. We show that research has been done in the field of irrigation and plant growth. In this regard, camera systems often provide images as training/input data for artificial intelligence-based systems. Finally, we provide a research agenda to pave the way for further research on the use of artificial intelligence in sustainable agriculture

Greenhouse environment: air and water monitoring

info:eu-repo/semantics/publishedVersio

Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review

A major objective in the design and operation of buildings is to maintain occupant comfort without incurring significant energy use. Particularly in narrower-plan buildings, the thermophysical properties and behaviour of their façades are often an important determinant of internal conditions. Building facades have been, and are being, developed to adapt their heat and mass transfer characteristics to changes in weather conditions, number of occupants and occupant’s requirements and preferences. Both the wall and window elements of a facade can be engineered to (i) harness solar energy for photovoltaic electricity generation, heating, inducing ventilation and daylighting (ii) provide varying levels of thermal insulation and (iii) store energy. As an adaptive façade may need to provide each attribute to differing extents at particular times, achieving their optimal performance requires effective control. This paper reviews key aspects of current and emerging adaptive façade technologies. These include (i) mechanisms and technologies used to regulate heat and mass transfer flows, daylight, electricity and heat generation (ii) effectiveness and responsiveness of adaptive façades, (iii) appropriate control algorithms for adaptive facades and (iv) sensor information required for façade adaptations to maintain desired occupants’ comfort levels while minimising the energy use

A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers

[EN] Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 degrees C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 degrees C and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Guillén-Navarro, MA.; Martínez-España, R.; Bueno-Crespo, A.; Morales-García, J.; Ayuso, B.; Cecilia-Canales, JM. (2020). A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers. Sensors. 20(24):1-15. https://doi.org/10.3390/s20247129S1152024Melgarejo-Moreno, J., López-Ortiz, M.-I., & Fernández-Aracil, P. (2019). Water distribution management in South-East Spain: A guaranteed system in a context of scarce resources. Science of The Total Environment, 648, 1384-1393. doi:10.1016/j.scitotenv.2018.08.263Ferrández-Pastor, F., García-Chamizo, J., Nieto-Hidalgo, M., & Mora-Martínez, J. (2018). Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors, 18(6), 1731. doi:10.3390/s18061731Liaghat. (2010). A Review: The Role of Remote Sensing in Precision Agriculture. American Journal of Agricultural and Biological Sciences, 5(1), 50-55. doi:10.3844/ajabssp.2010.50.55Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., … Willenbockel, D. (2013). Agriculture and climate change in global scenarios: why don’t the models agree. Agricultural Economics, 45(1), 85-101. doi:10.1111/agec.12091Crookston, R. K. (2006). A Top 10 List of Developments and Issues Impacting Crop Management and Ecology During the Past 50 Years. Crop Science, 46(5), 2253-2262. doi:10.2135/cropsci2005.11.0416gasDutta, R., Morshed, A., Aryal, J., D’Este, C., & Das, A. (2014). Development of an intelligent environmental knowledge system for sustainable agricultural decision support. Environmental Modelling & Software, 52, 264-272. doi:10.1016/j.envsoft.2013.10.004Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918-929. doi:10.1016/j.jhydrol.2018.04.065Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, 53(5), 3878-3895. doi:10.1002/2016wr019933Coopersmith, E. J., Minsker, B. S., Wenzel, C. E., & Gilmore, B. J. (2014). Machine learning assessments of soil drying for agricultural planning. Computers and Electronics in Agriculture, 104, 93-104. doi:10.1016/j.compag.2014.04.004Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., & Gocic, M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, 117, 214-225. doi:10.1016/j.compag.2015.08.008Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017). Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136, 71-78. doi:10.1016/j.compag.2017.01.027Jin, X.-B., Yu, X.-H., Wang, X.-Y., Bai, Y.-T., Su, T.-L., & Kong, J.-L. (2020). Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability, 12(4), 1433. doi:10.3390/su12041433Castañeda-Miranda, A., & Castaño-Meneses, V. M. (2020). Internet of things for smart farming and frost intelligent control in greenhouses. Computers and Electronics in Agriculture, 176, 105614. doi:10.1016/j.compag.2020.105614Tzounis, 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.2017.09.007Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833Jawad, H., Nordin, R., Gharghan, S., Jawad, A., & Ismail, M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors, 17(8), 1781. doi:10.3390/s17081781Guillén‐Navarro, M. A., Martínez‐España, R., López, B., & Cecilia, J. M. (2019). A high‐performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience, 33(2). doi:10.1002/cpe.5299Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-

Aplicación de Inteligencia Artificial sobre infraestructuras IoT para automatizar y optimizar los procesos de agricultura intensiva en invernaderos.

La agenda de desarrollo sostenible (Sustainable Development Goals, SDG) de las Naciones Unidas establece una serie de objetivos con el fin de erradicar la pobreza, proteger el planeta y asegurar la prosperidad de sus ciudadanos. Entre estos objetivos se destacan: (6) “Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos”, (13) “Adoptar medidas urgentes para combatir el cambio climático y sus efectos” y (15) “Proteger, restaurar y promover el uso sostenible de los ecosistemas terrestres, gestionar de forma sostenible los bosques, luchar contra la desertización, detener e invertir la degradación del suelo y frenar la pérdida de biodiversidad”. Los procesos industriales y, en concreto, los procesos de agricultura intensiva, son una de las principales amenazas para cumplir con los SDGs. Sin embargo, los avances tecnológicos en materias como la Inteligencia Artificial (Artificial Intelligence, AI), la Computación de Alto Rendimiento (High Performance Computing, HPC) o el Internet de las Cosas (Internet of Things, IoT) permiten aumentar la productividad de estos procesos reduciendo su impacto medioambiental y ecosistémico. La investigación desarrollada en la presente tesis doctoral pretende establecer un marco de trabajo donde aprovechar los avances tecnológicos desarrollados en estas disciplinas, es decir, AI, HPC e IoT, para optimizar y reducir el impacto de los procesos industriales más agresivos para el medioambiente. En concreto, esta tesis doctoral se desarrollará en el contexto de agricultura intensiva en invernaderos, un sector de un gran valor estratégico, comercial e incluso humanitario para garantizar el acceso a los alimentos a toda la humanidad, centrándose en tres puntos clave: (1) la generación de técnicas de AI de bajo consumo que puedan ser ejecutados en plataformas con reducidas capacidades de cómputo, tales como dispositivos IoT; (2) la creación de una infraestructura que permite entrenar, desplegar y predecir con técnicas de AI que requieren de grandes capacidades de cómputo en pequeños dispositivos IoT gracias a protocolos de comunicación en tiempo real como MQTT; y (3) el aumento de las capacidades de cómputo y la eficiencia energética de los dispositivos IoT gracias a la virtualización de GPUs remotas mediante rCUDA. Los principales resultados obtenidos en relación a lo expuesto anteriormente demuestran que (1) la intersección entre la AI, HPC e IoT es todavía muy incipiente. Las cargas de cómputo del aprendizaje máquina son cada vez más altas y se diverge cada vez más de los recursos computacionales disponibles en los dispositivos de cómputo más cercanos a la captura de datos, es decir, los dispositivos de Edge Computing. Estas plataformas no son computacionalmente capaces de desarrollar parte de las tareas más exigentes (como, por ejemplo, el entrenamiento de técnicas de AI), limitando el éxito de su aplicación; (2) se puede crear una infraestructura auxiliar que permita desarrollar predicciones en tiempo real en dispositivos IoT, aunque el intercambio de información entre los distintos nodos de la infraestructura conlleva una latencia asumible puesto que es muy reducida; y (3) es posible ampliar las capacidades computacionales y la eficiencia energética de los dispositivos IoT mediante el uso de técnicas de virtualización de GPUs remotas. Estas técnicas aumentan notablemente la eficiencia energética de estos dispositivos ya que se delega las operaciones de mayor carga computacional a los servidores remotos de cómputo. Si bien es cierto, el consumo total de la infraestructura aumenta notablemente a causa del gasto en comunicaciones entre los dispositivos edge y cloud. Para finalizar, destacar que la presente tesis se ha desarrollado en el proyecto retos-colaboración “Desarrollo de infraestructuras IoT de altas prestaciones contra el cambio climático basadas en inteligencia artificial” (GLOBALoT) con referencia RTC2019-007159-5, financiado por el Ministerio de Ciencia e Innovación / Agencia Estatal de Investigación, que tiene un marcado carácter tecnológico y, por tanto, se ha transferido el conocimiento obtenido, desarrollando un prototipo funcional en TRL 3-4 que ha sido desplegado en un entorno real de invernadero ofrecido por uno de los socios del proyecto, la empresa NUTRICONTROL. Los resultados obtenidos muestran un claro interés por esta tecnología, sentando las bases para automatizar y optimizar procesos mediante la Inteligencia Artificial de las Cosas (Artificial Intelligence of Things, AIoT) para aumentar la producción y reducir el impacto medioambiental en invernaderos inteligentes.Ingeniería, Industria y Construcció

Automatización y control de tecnologías implementadas en invernaderos: una revisión

Protected agriculture is a way of producing food by creating a microclimate that allows protecting a crop from the risks inherent in free exposure; in this sense, its purpose is to guarantee the optimal and appropriate conditions of internal variables to generate reproduction, development and growth of plants with quality and commercial opportunity. In this way, the application of technologies to crops has extended considerably due to the need to optimize this productive alternative: in this respect, there are multiple scattered investigations based on particular designs of elements such as greenhouses. Therefore, this article shows a review on protected agriculture aimed at the automation of greenhouses in countries that have implemented emerging technologies in this field and the consequent control generated in the stages of the production cycle through sensors, actuators, specific covers or robots designed to perform tasks such as spraying or harvesting, among others. Key analysis elements are presented on the modeling of the phenomenon that underlies the implementations, so that systems with the necessary adaptation are achieved for any crop, taking into account its type, cost and location, defining a baseline on the technologies that make it functional and efficient.La agricultura protegida es una manera de producir alimentos creando un microclima que permite proteger un cultivo de los riesgos propios de la libre exposición; en este sentido, tiene como finalidad garantizar las condiciones óptimas y apropiadas de variables internas para generar la reproducción, desarrollo y crecimiento de plantas con calidad y oportunidad comercial. De esta manera, la aplicación de tecnologías a cultivos se ha extendido considerablemente por la necesidad de optimizar esta alternativa productiva: al respecto se encuentran múltiples investigaciones dispersas basadas en diseños particulares de elementos como los invernaderos. Por lo anterior, el presente artículo muestra una revisión sobre agricultura protegida orientada a la automatización de invernaderos en los países que han realizado implementaciones de tecnologías emergentes en este campo y el consecuente control generado en las etapas del ciclo productivo a través de sensores, actuadores, cubiertas específicas o robots diseñados para realizar tareas tales como fumigación o cosechado, entre otras. Se presentan elementos de análisis clave sobre el modelamiento del fenómeno que subyace a las implementaciones, de manera que se logren sistemas con la adaptación necesaria para cualquier cultivo teniendo en cuenta su tipo, costo y ubicación, definiendo una línea de base sobre las tecnologías que lo hacen funcional y eficiente

A high-performance IoT solution to reduce frost damages in stone fruits

[EN] Agriculture is one of the key sectors where technology is opening new opportunities to break up the market. The Internet of Things (IoT) could reduce the production costs and increase the product quality by providing intelligence services via IoT analytics. However, the hard weather conditions and the lack of connectivity in this field limit the successful deployment of such services as they require both, ie, fully connected infrastructures and highly computational resources. Edge computing has emerged as a solution to bring computing power in close proximity to the sensors, providing energy savings, highly responsive web services, and the ability to mask transient cloud outages. In this paper, we propose an IoT monitoring system to activate anti-frost techniques to avoid crop loss, by defining two intelligent services to detect outliers caused by the sensor errors. The former is a nearest neighbor technique and the latter is the k-means algorithm, which provides better quality results but it increases the computational cost. Cloud versus edge computing approaches are analyzed by targeting two different low-power GPUs. Our experimental results show that cloud-based approaches provides highest performance in general but edge computing is a compelling alternative to mask transient cloud outages and provide highly responsive data analytic services in technologically hostile environments.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. Finally, we thank the farmers for the availability of their resources to be able to asses and improve the IoT monitoring system proposed.Guillén-Navarro, MA.; Martínez-España, R.; López, B.; Cecilia-Canales, JM. (2021). A high-performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience (Online). 33(2):1-14. https://doi.org/10.1002/cpe.529911433