Image-based Weed Classification

Image-Based Weed Classification is a computational method that identifies and categorizes pasture weed species through the analysis of visual features extracted from digital images, distinguishing itself from other classification approaches by relying primarily on pixel-level data and pattern recognition algorithms.

211

Publications

13.4K

Citations

689

Authors

272

Institutions

Publications per year

2017–2026

137

Authors

689

  1. SI
    S. Imran Moazzam

    Technological University Dublin, Ireland

    4 publications · 147 citations

    5

    h-index

  2. WS
    Waqar S. Qureshi

    National University of Sciences and Technology, Pakistan

    4 publications · 147 citations

    5

    h-index

  3. MM
    Mohd Marzuki Mustafa

    National University of Malaysia, Malaysia

    5 publications · 252 citations

    5

    h-index

  4. SO
    Shiv O. Prasher

    McGill University, Canada

    5 publications · 252 citations

    5

    h-index

  5. US
    Umar Shahbaz Khan

    National University of Sciences and Technology, Pakistan

    4 publications · 147 citations

    5

    h-index

1–5 of 689

Venues

  1. Computers and Electronics in Agriculture

    21 publications · 2.6K citations

    20

    h-index

  2. Remote Sensing

    11 publications · 845 citations

    11

    h-index

  3. Transactions of the ASAE

    8 publications · 2.1K citations

    8

    h-index

  4. Biosystems Engineering

    7 publications · 389 citations

    7

    h-index

  5. Smart Agricultural Technology

    6 publications · 533 citations

    7

    h-index

1–5