WeedScout: Real-Time Autonomous Blackgrass Classification and Mapping Using Dedicated Hardware (original) (raw)

Abstract

Blackgrass (Alopecurus myosuroides) is a competitive weed that has wide-ranging impacts on food security by reducing crop yields and increasing cultivation costs. In addition to the financial burden on agriculture, the application of herbicides as a preventive to blackgrass can negatively affect access to clean water and sanitation. The WeedScout project introduces a Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a cutting-edge solution tailored for real-time detection of blackgrass, for precision weed management practices. Leveraging Artificial Intelligence (AI) algorithms, the system processes live image feeds, infers blackgrass density, and covers two stages of maturation. The research investigates the deployment of You Only Look Once (YOLO) models, specifically the streamlined You Only Look Once (YOLO)v8 and YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By optimising inference speed and model performance, the project advances the integration of AI into agricultural practices, offering potential solutions to challenges such as herbicide resistance and environmental impact. Additionally, two datasets and model weights are made available to the research community, facilitating further advancements in weed detection and precision farming technologies.

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Authors and Affiliations

  1. Computational Intelligence Applications Research Group, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK
    Matthew Gazzard, Isibor Kennedy Ihianle, Jordan J. Bird & Pedro Machado
  2. School of Animal, Rural and Environmental Sciences, Nottingham Trent University, Brackenhurst, Southwell, NG25 0QF, UK
    Helen Hicks
  3. Mediprospects AI, 5-7 High Street, London, E13 0AD, UK
    Md Mahmudul Hasan

Authors

  1. Matthew Gazzard
  2. Helen Hicks
  3. Isibor Kennedy Ihianle
  4. Jordan J. Bird
  5. Md Mahmudul Hasan
  6. Pedro Machado

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Correspondence toPedro Machado .

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Editors and Affiliations

  1. Department of Electronic and Electrical Engineering, Brunel University London, London, UK
    M. Nazmul Huda
  2. Department of Mechanical and Aerospace Engineering, Brunel University London, London, UK
    Mingfeng Wang
  3. Department of Electronic and Electrical Engineering, Brunel University London, London, UK
    Tatiana Kalganova

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Gazzard, M., Hicks, H., Ihianle, I.K., Bird, J.J., Hasan, M.M., Machado, P. (2025). WeedScout: Real-Time Autonomous Blackgrass Classification and Mapping Using Dedicated Hardware. In: Huda, M.N., Wang, M., Kalganova, T. (eds) Towards Autonomous Robotic Systems. TAROS 2024. Lecture Notes in Computer Science(), vol 15051. Springer, Cham. https://doi.org/10.1007/978-3-031-72059-8\_34

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