Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification
2026-06-22 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors developed a new computer method to identify plant leaf diseases more accurately, even when pictures are complicated or unevenly lit. They combined three different types of models—EfficientNet-B0, DenseNet-121, and Swin-Tiny—so the system can look at the leaves in different ways, both up close and from afar. Their approach uses a smart way to decide which model's opinion to trust more depending on the leaf image. Tests on potato, durian, and sesame leaf diseases showed better detection scores than using any single model alone, suggesting their method works well across different plants. This could help farmers monitor crops more reliably in real life.
Plant leaf disease classificationMixture-of-Experts (MoE)EfficientNet-B0DenseNet-121Swin-TinySoft gating mechanismCross-architectural routingClass imbalanceMulti-scale featuresPrecision agriculture
Authors
Phi-Hung Hoang, Thi-Thu-Hong Phan
Abstract
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