融合多架构专家模型，提升复杂环境下植物病叶识别准确率
Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification
- 跨架构专家路由，动态分配EfficientNet、DenseNet与Swin-Tiny的权重
- 在马铃薯病叶数据集上达91.68%召回率与92.62%F1-score
- 适合农业图像识别、小样本病害检测等实际应用场景
植物叶片病害分类对作物保护和精准农业至关重要，但在复杂背景、光照变化及严重类别不平衡下仍具挑战。单一架构模型难以同时捕捉局部与全局特征。为此，本文提出一种自适应软性混合专家（MoE）框架，集成EfficientNet-B0、DenseNet-121与Swin-Tiny，利用其互补的多尺度、局部与全局特征。通过软门控机制实现输入依赖的专家加权，结合两阶段精炼训练策略提升优化稳定性与泛化能力。在高度不平衡的马铃薯叶片病害数据集上，取得91.68%召回率和92.62% F1-score，较最强单个专家分别提升5.91%和5.03%。在榴莲与芝麻叶片病害数据集上分别达到94.03%与97.04% F1-score，验证了框架的跨数据集泛化能力，展现出可靠的实际作物健康监测潜力。
原文摘要 · Abstract (English)
Plant leaf disease classification is crucial for crop protection and precision agriculture but remains challenging under complex backgrounds, illumination variations, and severe class imbalance. Moreover, single-architecture models often fail to effectively capture both local and global representations. To address these challenges, this study proposes an adaptive soft Mixture-of-Experts (MoE) framework with cross-architectural routing that integrates EfficientNet-B0, DenseNet-121, and Swin-Tiny to exploit complementary multi-scale, local, and global features. A soft gating mechanism dynamically assigns input-dependent expert weights, while a two-stage refinement training strategy improves optimization stability and generalization. Experiments on a highly imbalanced potato leaf disease dataset achieve 91.68% recall and 92.62% F1-score, surpassing the strongest individual expert by 5.91% and 5.03%, respectively. Additional evaluations on durian and sesame leaf disease datasets yield F1-scores of 94.03% and 97.04%, demonstrating robust cross-dataset generalization and the potential of the proposed framework for reliable real-world crop health monitoring
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