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Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review

Author

Listed:
  • Shirin Ghatrehsamani

    (Department of Agricultural and Biological Engineering, Penn State University, State College, PA 16802, USA
    Department of Agricultural and Technology Education, Montana State University, Bozeman, MT 59717, USA)

  • Gaurav Jha

    (Department of Agricultural and Technology Education, Montana State University, Bozeman, MT 59717, USA
    Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA)

  • Writuparna Dutta

    (Multitrophic Interactions and Biocontrol Research Laboratory, Department of Life Sciences, Presidency University, Kolkata 700073, West Bengal, India)

  • Faezeh Molaei

    (Department of Mechanical Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran)

  • Farshina Nazrul

    (Department of Electrical & Computer Engineering, Montana State University, Bozeman, MT 59717, USA)

  • Mathieu Fortin

    (Department of Computer Science and Software Engineering, Laval University, Quebec, QC G1V 0A6, Canada)

  • Sangeeta Bansal

    (Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA)

  • Udit Debangshi

    (Institute of Agriculture, Visva-Bharati University, Sriniketan 700073, West Bengal, India)

  • Jasmine Neupane

    (Department of Agricultural and Technology Education, Montana State University, Bozeman, MT 59717, USA)

Abstract

The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world.

Suggested Citation

  • Shirin Ghatrehsamani & Gaurav Jha & Writuparna Dutta & Faezeh Molaei & Farshina Nazrul & Mathieu Fortin & Sangeeta Bansal & Udit Debangshi & Jasmine Neupane, 2023. "Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1843-:d:1039781
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    References listed on IDEAS

    as
    1. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    2. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
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