arXiv (All)
open access
Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification
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 representati
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification#299976
- Comprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion.#988450
- Grape leaf multi-disease detection with confidence value using transfer learning integrated to regions with convolutional neural networks#410787
- GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.#305105
- A comparative analysis of single- and dual-backbone deep learning architectures with explainable AI for cherry leaf disease classification.#285147
Record · ID 1117478
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive