Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

Journal: Pest management science
PMID: 38323798

Abstract

BACKGROUND: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds.

Authors

  • Hu Sun
    Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China.
  • Teng Liu
    National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Institute of Technology, Beijing, China.
  • Jinxu Wang
    Peking University Institute of Advanced Agricultural Sciences/Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China.
  • Danlan Zhai
    Peking University Institute of Advanced Agricultural Sciences / Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China.
  • Jialin Yu
    Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.

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