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Mapping Agricultural Soil in Greenhouse Using an Autonomous Low-Cost Robot and Precise Monitoring

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)

  • Fatma Taher

    (College of Technological Innovation, Zayed University, Dubai 144534, United Arab Emirates)

  • Abdelhafid El Ouardi

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

  • 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

Our work is focused on developing an autonomous robot to monitor greenhouses and large fields. This system is designed to operate autonomously to extract useful information from the plants based on precise GPS localization. The proposed robot is based on an RGB camera for plant detection and a multispectral camera for extracting the different special bands for processing, and an embedded architecture integrating a Nvidia Jetson Nano, which allows us to perform the required processing. Our system uses a multi-sensor fusion to manage two parts of the algorithm. Therefore, the proposed algorithm was partitioned on the CPU-GPU embedded architecture. This allows us to process each image in 1.94 s in a sequential implementation on the embedded architecture. The approach followed in our implementation is based on a Hardware/Software Co-Design study to propose an optimal implementation. The experiments were conducted on a tomato farm, and the system showed that we can process different images in real time. The parallel implementation allows to process each image in 36 ms allowing us to satisfy the real-time constraints based on 5 images/s. On a laptop, we have a total processing time of 604 ms for the sequential implementation and 9 ms for the parallel processing. In this context, we obtained an acceleration factor of 66 for the laptop and 54 for the embedded architecture. The energy consumption evaluation showed that the prototyped system consumes a power between 4 W and 8 W. For this raison, in our case, we opted a low-cost embedded architecture based on Nvidia Jetson Nano.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15539-:d:980748
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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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    1. 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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