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

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  • نوع التسجيلة:
    Electronic Resource
  • الدخول الالكتروني :
    http://hdl.handle.net/10251/167858
  • معلومة اضافية
    • Publisher Information:
      MDPI AG Hispana 2020-12
    • Added Details:
      Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
      Agencia Estatal de Investigación
      Ministerio de Economía y Competitividad
      Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia
      Generalitat Valenciana
      Guillén-Navarro, Miguel A.
      Martínez-España, Raquel
      Bueno-Crespo, Andrés
      Morales-García, Juan
      Ayuso, Belén
      Cecilia-Canales, José María
    • نبذة مختصرة :
      [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.
    • الموضوع:
      Multivariate LSTM based approach; IoT system; Intelligent systems; Precision agriculture; ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES; info:eu-repo/semantics/article
    • Note:
      TEXT
      English
    • Other Numbers:
      UPV oai:riunet.upv.es:10251/167858
      info:doi:10.3390/s20247129
      urn:eissn:1424-8220
      33322717
      PMC7764077
      https://riunet.upv.es/bitstream/10251/167858/2/Gullén-Navarro;Martínez-España;Bueno-Crespo - A Decson Support System for Water Optmzatonn Ant-Fr....pdf.jpg
      1258892937
    • Contributing Source:
      UNIVERSITAT POLITECNICA DE VALENCIA
      From OAIster®, provided by the OCLC Cooperative.
    • الرقم المعرف:
      edsoai.on1258892937
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