Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification

• URL: http://arxiv.org/abs/2606.23441v1
• Date: Mon, 22 Jun 2026 14:58:42 GMT
• Title: Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification
• Abstract summary: This study proposes an adaptive soft Mixture-of-Experts (MoE) framework with cross-architectural routing.<n>MoE framework integrates EfficientNet-B0, DenseNet-121, and Swin-Tiny to exploit complementary multi-scale, local, and global features.
• Score: 0.06875312133832077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

Related papers

• Robustifying pathology foundation models via fine-tuning [39.255502531644204]
  Pathology foundation models (FMs) produce powerful tile-level representations which remain sensitive to scanner and staining variability.<n>We develop a novel fine-tuning recipe that improves the robustness of pathology FMs to acquisition factors.
  arXiv  Detail & Related papers  (2026-07-24T19:05:21Z)
• Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift [2.820120779484705]
  Climate change increases abiotic stress and biotic pressures, including pests and diseases, which often produce visually similar foliar symptoms.<n>We propose an image-based classification approach for peach leaf damage detection.
  arXiv  Detail & Related papers  (2026-06-01T10:36:01Z)
• AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification [45.52399819498234]
  This work presents a systematic empirical comparison of three model paradigms for fine-grained crop disease classification.<n>We introduce AgriPath-LF16, a benchmark containing 111k images spanning 16 crops and 41 diseases.<n>CNNs achieve the highest accuracy on lab imagery but degrade under domain shift.<n> Contrastive VLMs provide a robust and parameter-efficient alternative with competitive cross-domain performance.
  arXiv  Detail & Related papers  (2026-03-08T17:28:01Z)
• Dynamic Meta-Ensemble Framework for Efficient and Accurate Deep Learning in Plant Leaf Disease Detection on Resource-Constrained Edge Devices [0.0]
  We introduce a novel Dynamic Meta-Enemble Framework (DMEF) for high-accuracy plant disease diagnosis under resource constraints.<n>DMEF employs an adaptive weighting mechanism that dynamically combines the predictions of three lightweight convolutional neural networks.<n>Experiments on benchmark datasets for potato and maize diseases demonstrate state-of-the-art classification accuracies of 99.53% and 96.61%, respectively.
  arXiv  Detail & Related papers  (2026-01-24T03:57:49Z)
• A Domain-Adapted Lightweight Ensemble for Resource-Efficient Few-Shot Plant Disease Classification [0.0]
  We present a few-shot learning approach that combines domain-adapted MobileNetV2 and MobileNetV3 models as feature extractors.<n>For the classification task, the fused features are passed through a Bi-LSTM classifier enhanced with attention mechanisms.<n>It consistently improved performance across 1 to 15 shot scenarios, reaching 98.23+-0.33% at 15 shot.<n> Notably, it also outperformed the previous SOTA accuracy of 96.4% on six diseases from PlantVillage, achieving 99.72% with only 15-shot learning.
  arXiv  Detail & Related papers  (2025-12-15T15:17:29Z)
• TCLeaf-Net: a transformer-convolution framework with global-local attention for robust in-field lesion-level plant leaf disease detection [13.963787476506292]
  We release Daylily-Leaf, a paired lesion-level dataset comprising 1,746 RGB images and 7,839 lesions captured under both ideal and in-field conditions.<n>We propose TCLeaf-Net, a transformer-convolution hybrid detector optimized for real-field use.
  arXiv  Detail & Related papers  (2025-12-13T15:03:48Z)
• CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design [46.12506067241116]
  We present CODE (Chain of Diffusion Embeddings), a self evaluating metric to quantify topological frustration.<n>We propose CONFIDE, a unified evaluation framework that combines energetic and topological perspectives.<n>By combining data driven embeddings with theoretical insight, CODE and CONFIDE outperform existing metrics across a wide range of biomolecular systems.
  arXiv  Detail & Related papers  (2025-11-20T03:38:46Z)
• Heterogeneity-Aware Client Sampling: A Unified Solution for Consistent Federated Learning [31.50593149242509]
  Federated learning (FL) commonly involves clients with diverse communication and computational capabilities.<n>We reveal the fundamentally distinct mechanisms through which heterogeneous communication and computation drive inconsistency in FL.<n>We propose Federated Heterogeneity-Aware Client Sampling, FedACS, a universal method to eliminate all types of objective inconsistency.
  arXiv  Detail & Related papers  (2025-05-16T14:31:36Z)
• Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss [38.70308073598037]
  classification tasks often suffer from severe class imbalances, short and unstructured text, and overlapping semantic categories.<n>We present our system for SemEval- 2025 Task 9: Food Hazard Detection, which ad- dresses these issues by applying data augmenta- tion techniques to improve classification perfor- mance.
  arXiv  Detail & Related papers  (2025-04-24T16:35:44Z)
• Design and Implementation of FourCropNet: A CNN-Based System for Efficient Multi-Crop Disease Detection and Management [3.4161054453684705]
  This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops.<n>FourCropNet achieved the highest accuracy of 99.7% for Grape, 99.5% for Corn, and 95.3% for the combined dataset.
  arXiv  Detail & Related papers  (2025-03-11T12:00:56Z)
• Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks [43.723206630188656]
  Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks.<n>We propose Federated hyBrid Adversarial training and self-adversarial disTillation (FedBAT)<n>FedBAT integrates hybrid adversarial training and self-adversarial distillation into the conventional FL framework.
  arXiv  Detail & Related papers  (2024-12-26T21:32:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.