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Review of Weed Detection Methods Based on Computer Vision.

Zhangnan Wu1, Yajun Chen1, Bo Zhao2

  • 1Department of Information Science, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

Accurate weed detection using computer vision is crucial for precision agriculture. This review covers traditional and deep learning methods for identifying weeds, aiming to reduce chemical herbicide use and environmental impact.

Keywords:
computer visiondeep learningimage processingmachine learningweed detection

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Environmental Science

Background:

  • Weeds significantly impact agricultural productivity and necessitate effective management strategies.
  • Widespread chemical herbicide application leads to environmental pollution and ecological damage.
  • Precision agriculture requires accurate crop-weed discrimination for targeted interventions.

Purpose of the Study:

  • To review and analyze traditional image processing and deep learning methods for weed detection.
  • To evaluate the advantages and disadvantages of current weed identification techniques.
  • To explore relevant datasets, weeding machinery, and future research directions in weed detection.

Main Methods:

  • Review of traditional image processing techniques for plant identification.
  • Analysis of deep learning-based computer vision approaches for weed detection.
  • Examination of existing weed and crop leaf datasets and weeding machinery.

Main Results:

  • Identification of various computer vision methods applied to weed detection.
  • Comparative analysis of the strengths and weaknesses of different detection approaches.
  • Overview of resources including datasets and machinery relevant to weed management.

Conclusions:

  • Computer vision, particularly deep learning, offers promising solutions for accurate weed detection.
  • Addressing current challenges in weed detection is essential for advancing precision agriculture.
  • Future research should focus on improving detection accuracy and integrating systems for efficient weed management.