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Fusing Perceptual Vision Experts with Multimodal Large Language Models for Explainable Plant Disease Diagnosis: From Benchmark Imagery to Real-World Robotic Field Validation
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multimodalexplainable AIplant disease diagnosisrobotic field validation
2608.24934
Builder Relevance
3h ago80%
Abstract
The paper presents a framework for accurate plant disease diagnosis by fusing perceptual evidence with multimodal language models.
Reality Card
Core Claim
The Hybrid Hierarchical Multi-Agent Framework (H$^{2}$MAF) improves plant disease diagnosis accuracy significantly, achieving up to 99.3% accuracy in real-world conditions.
Method / Result
Gemma improves accuracy from 63.9% to 68.5% on the PlantDoc dataset.
Limitations
Calibration dependency of MLLM arbitration may affect reproducibility.
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