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An Attention-Enhancing Cascading RetNet Framework for Vegetation Conservation

Xuhui Li,Yicheng Liu,Xiaolou Chen,Rui Wang,Nanjun Zhou,Guangchun Yang

Published 2025 in International Conference on Computer Supported Cooperative Work in Design

ABSTRACT

Vegetation protection is a crucial task in agricultural production. Due to the diversity of tree species and their varied growth conditions, the demand for protective ash spray varies greatly, posing multifaceted challenges of experience and technology for manual operations. This paper proposes a framework that integrates an attention mechanism with RetNet for predicting tree growth conditions. It extracts and fuses core visual features from both tree species and growth conditions to achieve high-quality classification of tree growth status, thereby guiding the concentration of ash spray. Quantitative experimental results demonstrate that the proposed approach outperforms the baseline in accuracy, recall, and other metrics. These results indicate that the proposed framework is helpful for the optimization of vegetation protection workflow in the context of human-machine collaboration.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    International Conference on Computer Supported Cooperative Work in Design

  • Publication date

    2025-05-05

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/CSCWD64889.2025.11033574
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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