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Updated: Jan 28, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Deep learning-based approaches for weed detection in crops.

Hua Zhao1, Yan Wang1

  • 1School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China.

Frontiers in Plant Science
|January 26, 2026
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Summary

Deep learning significantly enhances weed detection accuracy and scalability in agriculture. This review synthesizes deep learning models for precision weed management, addressing current challenges and future opportunities.

Keywords:
deep learningimage classificationimage segmentationobject detectionweed detection

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Area of Science:

  • Agricultural Technology
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional machine vision for weed detection faces limitations in robustness and accuracy.
  • Deep learning offers superior performance in scalability and recognition for weed identification.

Purpose of the Study:

  • To provide a comprehensive review of deep learning-based weed detection methods.
  • To analyze the strengths, limitations, and challenges of current deep learning approaches in agriculture.
  • To highlight future directions for intelligent weeding systems.

Main Methods:

  • Focus on three major deep learning model families: object detection, image segmentation, and image classification.
  • Summarize and compare representative architectures, algorithmic features, and agricultural applications.
  • Critically analyze spatial localization, pixel-level delineation, computational efficiency, and model generalization.

Main Results:

  • Deep learning models show significant advantages over traditional methods in weed detection.
  • Key challenges include dataset scarcity, annotation costs, and real-time deployment.
  • Emerging solutions involve indirect detection, semi-supervised learning, and model-actuator integration.

Conclusions:

  • Deep learning is a transformative technology for modern weed detection.
  • Future opportunities lie in scalable, data-efficient, and precision-integrated weed management.
  • Guidance is offered for developing next-generation intelligent weeding systems.