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Related Experiment Video

Updated: May 24, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Attention-enhanced multi-task learning for binary segmentation and fine-grained aquatic plant classification in UAV

Ashifur Rahman1,2, M M Mahbubul Syeed3,4, Razib Hayat Khan1,2

  • 1Department of Computer Science and Engineering, Independent University, Bangladesh, Dhaka, 1229, Bangladesh.

Scientific Reports
|May 22, 2026
PubMed
Summary

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This study introduces an attention-enhanced multi-task learning framework for simultaneous aquatic vegetation segmentation and species classification from drone imagery. The model achieves high accuracy and efficiency, aiding biodiversity monitoring and ecosystem management.

Area of Science:

  • Environmental Monitoring
  • Remote Sensing
  • Computer Vision

Background:

  • Accurate aquatic vegetation monitoring via unmanned aerial vehicles (UAVs) is hindered by complex backgrounds and data limitations.
  • Existing methods often treat segmentation and classification separately, limiting integrated species-level analysis.

Purpose of the Study:

  • To develop an attention-enhanced multi-task learning framework for simultaneous binary segmentation and 14-class species classification of aquatic vegetation.
  • To enable unified structural and semantic understanding of aquatic ecosystems from UAV imagery.

Main Methods:

  • A shared encoder with attention-guided skip connections and joint optimization strategy was employed.
  • The framework simultaneously performs segmentation and classification, validated on a new UAV dataset from Bangladesh.

Related Experiment Videos

Last Updated: May 24, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

  • Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability.

    • Main Results:

    • The model achieved a Dice coefficient of 0.7344 and mIoU of 0.6904 for segmentation, with 98.77% classification accuracy.
    • Attention mechanisms and joint learning with Gaussian blur improved performance and feature discrimination.
    • The framework demonstrated a ~50% reduction in parameters and ~48.6% faster inference speed compared to single-task models.

    Conclusions:

    • The proposed framework offers a robust and efficient solution for integrated aquatic vegetation analysis from UAV data.
    • It is suitable for real-time applications, large-scale biodiversity monitoring, and invasive species detection.
    • The study highlights the importance of unified structural and semantic understanding for effective freshwater ecosystem management.