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A review on weed detection using ground-based machine vision and image processing techniques

Wang, Aichen ; Zhang, Wen ; Wei, Xinhua

Computers and electronics in agriculture, 2019-03, Vol.158, p.226-240 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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  • Título:
    A review on weed detection using ground-based machine vision and image processing techniques
  • Autor: Wang, Aichen ; Zhang, Wen ; Wei, Xinhua
  • Assuntos: Classification ; Color ; Crop yield ; Feature extraction ; Image detection ; Image processing ; Image segmentation ; Information management ; Machine learning ; Machine vision ; Mathematical morphology ; Occlusion ; Precision agriculture ; Site-specific weed management ; Vision systems ; Weed detection ; Weeds
  • É parte de: Computers and electronics in agriculture, 2019-03, Vol.158, p.226-240
  • Descrição: •Recent advances of weed detection using ground-based machine vision were reviewed.•Features for segmenting vegetation and detecting weeds from crops were elaborated.•Recently developed deep learning-based approaches for weed detection were covered.•Challenges and solutions for weed detection in the field were presented. Weeds are among the major factors that could harm crop yield. With the advances in electronic and information technologies, machine vision combined with image processing techniques has become a promising tool for precise real-time weed and crop detection in the field, providing valuable sensing information for site-specific weed management. This review summarized the advances of weed detection using ground-based machine vision and image processing techniques. Concretely, the four procedures, i.e., pre-processing, segmentation, feature extraction and classification, for weed detection were presented in detail. To separate vegetation from background, different color indices and classification approaches like color index-based, threshold-based and learning-based ones, were developed. The difficulty of weed detection lies in discriminating between crops and weeds that often have similar properties. Generally, four categories of features, i.e., biological morphology, spectral features, visual textures and spatial contexts, were used for the task, which were discussed in this review. Application of conventional machine learning-based and recently developed deep learning-based approaches for weed detection were also presented. Finally, challenges and solutions provided by researchers for weed detection in the field, including occlusion and overlap of leaves, varying lighting conditions and different growth stages, were discussed.
  • Editor: Amsterdam: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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