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Improving Sustainable Vegetation Indices Processing on Low-Cost Architectures

Author

Listed:
  • Amine Saddik

    (Laboratory of Systems Engineering and Information Technology LISTI, National School of Applied Sciences, Ibn Zohr University, Agadir 80000, Morocco)

  • Rachid Latif

    (Laboratory of Systems Engineering and Information Technology LISTI, National School of Applied Sciences, Ibn Zohr University, Agadir 80000, Morocco)

  • Abdelhafid El Ouardi

    (SATIE, CNRS, ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France)

  • Mohammed I. Alghamdi

    (Department of Engineering and Computer Sciences, Al-Baha University, Al-Baha 1988, Saudi Arabia)

  • Mohamed Elhoseny

    (College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates
    Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt)

Abstract

The development of embedded systems in sustainable precision agriculture has provided an important benefit in terms of processing time and accuracy of results, which has influenced the revolution in this field of research. This paper presents a study on vegetation monitoring algorithms based on Normalized Green-Red Difference Index (NGRDI) and Visible Atmospherically Resistant Index (VARI) in agricultural areas using embedded systems. These algorithms include processing and pre-processing to increase the accuracy of sustainability monitoring. The proposed algorithm was evaluated on a real database in the Souss Massa region in Morocco. The collection of data was based on unmanned aerial vehicles images hand data using four different agricultural products. The results in terms of processing time have been implemented on several architectures: Desktop, Odroid XU4, Jetson Nano, and Raspberry. However, this paper introduces a thorough study of the Hardware/Software Co-Design approach to choose the most suitable system for our proposed algorithm that responds to the different temporal and architectural constraints. The evaluation proved that we could process 311 frames/s in the case of low resolution, which gives real-time processing for agricultural field monitoring applications. The evaluation of the proposed algorithm on several architectures has shown that the low-cost XU4 card gives the best results in terms of processing time, power consumption, and computation flexibility.

Suggested Citation

  • Amine Saddik & Rachid Latif & Abdelhafid El Ouardi & Mohammed I. Alghamdi & Mohamed Elhoseny, 2022. "Improving Sustainable Vegetation Indices Processing on Low-Cost Architectures," Sustainability, MDPI, vol. 14(5), pages 1-29, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2521-:d:755698
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    References listed on IDEAS

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    1. Samuel C. A. Pereira, 2021. "On the precision of information," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 30(3), pages 569-584, August.
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    Cited by:

    1. Amine Saddik & Rachid Latif & Fatma Taher & Abdelhafid El Ouardi & Mohamed Elhoseny, 2022. "Mapping Agricultural Soil in Greenhouse Using an Autonomous Low-Cost Robot and Precise Monitoring," Sustainability, MDPI, vol. 14(23), pages 1-26, November.
    2. Amine Saddik & Rachid Latif & Abedallah Zaid Abualkishik & Abdelhafid El Ouardi & Mohamed Elhoseny, 2023. "Sustainable Yield Prediction in Agricultural Areas Based on Fruit Counting Approach," Sustainability, MDPI, vol. 15(3), pages 1-14, February.

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