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SegFormer-based nectar source segmentation in remote sensing imagery.

Mengting Dong1, Hao Cao1, Tian Zhao1

  • 1College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, Anhui, China.

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|October 17, 2025
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Summary

This study introduces an improved SegFormer model for automatically identifying nectar-producing plants using remote sensing. The enhanced model boosts identification accuracy, aiding beekeepers in efficient management and optimizing honey production.

Keywords:
SegFormerbeesdeep learningnectar-producing plantsremote sensingsemantic segmentation

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Computer Vision

Background:

  • Beekeepers require accurate spatial distribution data of nectar plants for effective management.
  • Current methods for identifying nectar-producing plants are often inefficient and lack precision.

Purpose of the Study:

  • To develop an automated system for identifying nectar-producing plants using remote sensing imagery.
  • To improve the accuracy and efficiency of nectar plant detection for beekeeping applications.

Main Methods:

  • Collected and preprocessed high-resolution satellite imagery.
  • Developed an improved segmentation model based on the SegFormer architecture.
  • Integrated CBAM attention, residual structures, and spatial feature enhancement for improved accuracy.

Main Results:

  • The improved model achieved a mean Intersection over Union (mIoU) of 91.05%, an increase from 89.31%.
  • Mean Pixel Accuracy (mPA) improved to 95.02% from 94.15%.
  • Achieved mean Precision and Recall of 95.40% and 95.02%, respectively.

Conclusions:

  • The proposed method significantly enhances nectar plant identification efficiency and accuracy.
  • Provides reliable technical support for precision beekeeping, smart agriculture, and ecological monitoring.
  • Facilitates optimization of bee colony migration and honey quality regulation.