Dual-Channel Attention and Category-Guided Distillation for Lightweight Plant Disease Recognition

Abstract

Plant disease recognition remains challenging due to complex imaging conditions, inter-class visual similarity, and the need to deploy accurate models on resource-constrained devices. Although convolutional neural networks (CNNs) are effective at local pattern extraction, their locality bias limits global context modeling. Transformer-based models alleviate this issue, yet many existing designs do not explicitly exploit the complementarity between spatial and channel interactions. To address this limitation, we propose a Dual-Channel Feature Enhancement Network (DC-FEN) as the teacher model, which integrates spatial attention and channel attention to capture lesion-sensitive spatial cues and discriminative channel semantics. To enable lightweight deployment, we further introduce a Category-Guided Knowledge Distillation (CGKD) framework that transfers both category-level semantic knowledge and feature-level relational knowledge from the teacher to a MobileNetV3 student. Experimental results show that the proposed distillation framework consistently improves the performance of the lightweight MobileNetV3 student over its original baseline.

Plant phenotyping relevance

植物病害の病徴画像から病害状態を認識する軽量な画像解析モデルと知識蒸留手法を開発しており、植物の病害表現型推定が研究の中心である。

Code and data availability

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