Work

Robust Fine-Grained Pest Classification via Boundary-Aware Attention and Growth-Stage Supervision

Year: 2026

Type: article

Abstract: Accurate pest identification plays a critical role in effective pest management and sustainable agricultural production. However, fine-grained pest classification is inherently challenging due to low inter-class separability, significant intra-class variability, and complex environmental interference. To address these challenges, we develop a boundary-aware channel-spatial attention network to strengthen discriminative feature learning while suppressing background noise. The proposed attention module enhances fine-grained structural and boundary cues to improve inter-class separability under cluttered field conditions. Furthermore, auxiliary label supervision based on pest growth stages is incorporated to model developmental variations and enhance intra-class consistency. Experiments on the IP102 dataset demonstrate that the proposed method consistently outperforms state-of-the-art baselines in classification accuracy, validating its effectiveness for fine-grained agricultural pest classification. These results highlight the potential of integrating boundary-aware attention mechanisms with growth-stage supervision for robust real-world pest classification.

Source: Insects

DOI: 10.3390/insects17040423

Authors: Xinliang Liu, Ruiming Zhu, Yuying Cao

Institutions: Northeast Agricultural University, Northeastern University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137