mlsanityData to 14 Oct 2026

AdapterMoE: A Two-Stage Hard-Routing Mixture-of-Experts Architecture for Multi-Crop Disease Recognition with Calibrated Rejection and Incremental Learning

arXiv 2608.08808 ↗First posted 9 Aug 2026cs.CVcited by 0

On the map: Mixture-of-experts → Vision mixture-of-experts

Abstract (arXiv)

Timely crop-disease identification is critical to food security. Multi-crop recognition suits Mixture-of-Experts (MoE), but conventional soft-routing MoE learns crop assignment freely end-to-end, letting a few experts dominate (expert collapse) with no semantic correspondence to crops, and facing high retraining costs, unstable rejection of non-target inputs, and a saturated accuracy ceiling. We shift the objective from accuracy toward a trade-off among deployment cost, scaling flexibility, and rejection stability, using deterministic hard routing. We propose AdapterMoE: a RouterHead classifies the crop and rejects non-target crops via a Maximum Softmax Probability threshold, with a dual-gate Energy+KNN out-of-distribution module catching distribution-shifted inputs; five per-crop Adapters atop a frozen EfficientNet-B0 backbone discriminate diseases, each calibrated via Temperature Scaling. Because experts are hard-isolated at the data level, the design avoids expert collapse and exposes an add_crop interface for local, per-crop updates instead of full retraining. On PlantVillage (5 crops, 26 classes), across a fair five-system comparison, AdapterMoE attains accuracy statistically indistinguishable from the best baselines (Macro-F1 within a 0.24-point band) while cutting training cost to about 9% of full-network baselines, expanding to a new crop in

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts. Builds on 24 of its 28 references that are in the map. First posted on arXiv 9 Aug 2026.

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24 of 28 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023From Sparse to Soft Mixtures of Experts2 Aug
  2. 2021Scaling Vision with Sparse Mixture of Experts10 Jun

Other references · 22

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  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
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  12. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
10 more
  1. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  3. 2020Energy-based Out-of-distribution Detectioncited by 1,299
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  6. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 219
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  10. 2024Taming False Positives in Out-of-Distribution Detection with Human Feedbackcited by 7

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  1. 2026Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification
  2. 2026TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis
  3. 2026SAGE: Scalable Agentic Grounded Evaluation for Crop Disease Diagnosis ↗
  4. 2026AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification
  5. 2025Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management
  6. 2025AgriDoctor: A Multimodal Intelligent Assistant for Agriculture
  7. 2026Agri-R1: Agricultural Reasoning for Disease Diagnosis via Automated-Synthesis and Reinforcement Learning
  8. 2025DS_FusionNet: Dynamic Dual-Stream Fusion with Bidirectional Knowledge Distillation for Plant Disease Recognition