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Fuzzy Logic Based Pasture Assessment Using Weed and Bare Patch Detection

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Smart and Sustainable Agriculture (SSA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1470))

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Abstract

Precision agriculture has provided supporting applications for farmers with the use of Artificial Intelligence (AI) for processing farming data. Pastures are one of the main sources for dairy farming that have a great share in economy of agriculture. Weeds are the main issue of pastures, which impose a huge cost to dairy farmers annually. This paper proposes designing a software framework based on a fuzzy logic system for pasture assessment and pasture clean-up. Once weeds and empty spots of any pasture reduce its productivity, we considered them as two uncertainties that affect the weed management process. Applying our system to any pasture can measure the weed density and bareness through images and score the state of pasture’s productivity. With the aid of our software framework we can produce 2D weed density maps, 2D bareness maps, and scoring maps, which provide a better insight into the pastures. The types of 2D maps and the yield score can help and support dairy farmers to schedule, organize, and manage pastoral weeds.

This paper received the certificate, title, and the award of the best paper in smart and sustainable agriculture conference in 21–22 June 2021, Paris, France

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Authors and Affiliations

  1. University of Auckland, Auckland CBD, Auckland, 1010, New Zealand

    Hossein Chegini, Fernando Beltran & Aniket Mahanti

Authors
  1. Hossein Chegini
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  2. Fernando Beltran
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  3. Aniket Mahanti
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Corresponding author

Correspondence to Hossein Chegini .

Editor information

Editors and Affiliations

  1. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

  2. Université Internationale de Rabat, Rabat, Morocco

    Mounir Ghogho

  3. Laboratoire LIGM UMR 8049 CNRS, ESIEE Paris, Noisy-le-Grand, France

    Éric Renault

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Chegini, H., Beltran, F., Mahanti, A. (2021). Fuzzy Logic Based Pasture Assessment Using Weed and Bare Patch Detection. In: Boumerdassi, S., Ghogho, M., Renault, É. (eds) Smart and Sustainable Agriculture. SSA 2021. Communications in Computer and Information Science, vol 1470. Springer, Cham. https://doi.org/10.1007/978-3-030-88259-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-88259-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88258-7

  • Online ISBN: 978-3-030-88259-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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