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An efficient employment of internet of multimedia things in smart and future agriculture

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Abstract

Efficiently managing the irrigation process has become necessary to utilize water stocks due to the lack of water resources worldwide. Parched plant leads into hard breathing process, which would result in yellowing leaves and sprinkles in the soil. In this work, yellowing leaves and sprinkles in the soil have been observed using multimedia sensors to detect the level of plant thirstiness in smart farming. We modified the IoT concepts to draw an inspiration towards the perspective vision of ’Internet of Multimedia Things’ (IoMT). This research focuses on the smart employment of internet of Multimedia sensors in smart farming to optimize the irrigation process. The concepts of image processing work with IOT sensors and machine learning methods to make the irrigation decision. sensors reading have been used as training data set indicating the thirstiness of the plants, and machine learning techniques including the state-of-the-art deep learning were used in the next phase to find the optimal decision. The conducted experiments in this research are promising and could be considered in any smart irrigation system. The experimental results showed that the use of deep learning proves to be superior in the Internet of Multimedia Things environment.

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Authors and Affiliations

  1. Department of Computer Science and CIS, Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman, Jordan

    Shadi AlZu’bi, Bilal Hawashin & Muhannad Mujahed

  2. Department of Computer Science, Jordan University of Science and Technology, Irbid, Jordan

    Yaser Jararweh

  3. Department of Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra, India

    Brij B. Gupta

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  1. Shadi AlZu’bi
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  2. Bilal Hawashin
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  5. Brij B. Gupta
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Correspondence to Shadi AlZu’bi.

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AlZu’bi, S., Hawashin, B., Mujahed, M. et al. An efficient employment of internet of multimedia things in smart and future agriculture. Multimed Tools Appl 78, 29581–29605 (2019). https://doi.org/10.1007/s11042-019-7367-0

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