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Sustainable Yield Prediction in Agricultural Areas Based on Fruit Counting Approach

Author

Listed:
  • Amine Saddik

    (Laboratory of Systems Engineering and Information Technology LISTI, National School of Applied Sciences, Ibn Zohr University Agadir, Agadir 80000, Morocco
    Laboratoire d’Innovation Durable et de Recherche Appliquée, International University of Agadir, Bab Al madina, Quartier Tillila, B.P. 8143, Agadir 80000, Morocco)

  • Rachid Latif

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

  • Abedallah Zaid Abualkishik

    (College of Computer and Information Technology, American University in the Emirates, Dubai P.O. Box 503000, 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 P.O. Box 27272, United Arab Emirates
    Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt)

Abstract

A sustainable yield prediction in agricultural fields is a very critical task that aims to help farmers have an idea about agricultural operations. Generally, we can find a variety of applications proposed for this purpose that include fruit counting. These applications are based on Artificial Intelligence, especially Deep Learning (DL) and Machine Learning (ML) approaches. These approaches give reliable counting accuracy, but the problem is the use of a large database to achieve the desired accuracy. That makes these approaches limited. For this reason, in this work, we propose a low-complexity algorithm that aims to count green and red apples based on our real dataset collected in the Moroccan region, Fes-Meknes. This algorithm allowed us to further increase sustainability in agricultural fields based on yield prediction. The proposed approach was based on HSV conversion and the Hough transform for fruit counting. The algorithm was divided into three blocks based on image acquisition and filtering for the first block. The second block is the conversion to HSV and the detection of fruits. Finally, the counting operation for the third block. Subsequently, we proposed an implementation based on the low-cost Raspberry system and a desktop. The results show that we can reach 15 fps in the case of the Raspberry architecture and 40 fps based on the desktop. Our proposed system can inform agricultural policy by providing accurate and timely information on crop production, which can be used to guide decisions on food supply and distribution.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2707-:d:1055478
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    References listed on IDEAS

    as
    1. Mengyang Liu & Delan Zhu & Xinxin Nie & Changyang Shi & Hongbin Tu & Bo Jing & Huiyi Fan & Hang Zhao, 2022. "Design and Testing of a Remote Control-Based Integrated Flow Regulation and Measurement Device," Sustainability, MDPI, vol. 14(23), pages 1-11, November.
    2. Elahi, Ehsan & Khalid, Zainab & Tauni, Muhammad Zubair & Zhang, Hongxia & Lirong, Xing, 2022. "Extreme weather events risk to crop-production and the adaptation of innovative management strategies to mitigate the risk: A retrospective survey of rural Punjab, Pakistan," Technovation, Elsevier, vol. 117(C).
    3. Valda Rondelli & Enrico Capacci & Bruno Franceschetti, 2022. "Evaluation of the Stability Behavior of an Agricultural Unmanned Ground Vehicle," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
    4. Elahi, Ehsan & Zhang, Hongxia & Lirong, Xing & Khalid, Zainab & Xu, Haiyun, 2021. "Understanding cognitive and socio-psychological factors determining farmers’ intentions to use improved grassland: Implications of land use policy for sustainable pasture production," Land Use Policy, Elsevier, vol. 102(C).
    5. 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.
    6. 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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