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Enhancing weed detection through knowledge distillation and attention mechanism.

Ali El Alaoui1,2, Hajar Mousannif1

  • 1Computer Science Department, Computer Systems Engineering Laboratory, Faculty of Sciences Semlalia Cadi Ayyad University, Marrakesh, Morocco.

Frontiers in Robotics and AI
|September 29, 2025
PubMed
Summary
This summary is machine-generated.

This study optimizes Vision Transformers (ViT) for agricultural robotics using knowledge distillation. The enhanced ViT model achieves high weed detection accuracy with significantly reduced computational costs.

Keywords:
deep learningprecision agriculturerobotic weed controlvision transformerweed detection

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Weeds compete with crops, reducing agricultural yields.
  • Vision Transformers (ViT) show promise for weed detection but face deployment challenges in resource-limited agricultural robotics due to high training costs and model size.
  • Traditional Convolutional Neural Networks (CNNs) are less effective than ViTs.

Purpose of the Study:

  • To optimize Vision Transformer (ViT) models for weed detection in agricultural robotics.
  • To address the computational constraints of ViTs, including model size and memory requirements.
  • To maintain high weed detection performance while reducing computational expense.

Main Methods:

  • Proposed a knowledge distillation method to optimize ViT models.
  • Used ResNet-50 as a teacher model to distill knowledge into a compacted ViT student model.
  • Facilitated parameter sharing and local receptive fields for the student model.

Main Results:

  • The student ViT model achieved a mean Average Precision (mAP) of 83.47% for weed detection.
  • The optimized model has only 5.7 million parameters, indicating minimal computational expense.
  • Significant improvements in the student model's performance and efficiency were observed.

Conclusions:

  • Knowledge distillation effectively optimizes ViT models for weed detection.
  • The proposed method successfully addresses computational limitations for deploying ViTs in agricultural robotics.
  • The optimized ViT model offers a viable solution for accurate and efficient weed detection in agriculture.