Fusing Perceptual Vision Experts with Multimodal LLMs for Explainable Plant Disease Diagnosis: From Benchmark Imagery to Real-World Robotic Field Validation

arXiv:2608.24934 · cs.CV, cs.AI · Submitted 2026-08-23 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-23

Updated: 2026-09-26

Code: https://github.com/Applied-AI-Research-Lab/Explainable-AI-Plant-Disease-Detection

Terminology

• Accuracy — 75× in this paper · explained in 1 episode
• Calibration — 26× in this paper · explained in 7 episodes
• Benchmark — 25× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Convolutional Neural Network — 4× in this paper
• Prompt Engineering — 4× in this paper · explained in 4 episodes
• Transfer Learning — 4× in this paper · explained in 10 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Artificial Intelligence — 3× in this paper
• Hallucination — 3× in this paper · explained in 3 episodes
• Multi-Agent Framework — 3× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• National Science Foundation — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• United States Department of Agriculture — 2× in this paper
• Agentic AI — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Critical-Risk Calibration Error — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• National Institute of Food and Agriculture — 1× in this paper
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Per-Layer Embeddings — 1× in this paper
• Provenance — 1× in this paper · explained in 3 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• An open access repository of images on plant health to enable the development of mobile disease diagnostics
• Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks

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