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

Researchers introduced the Hybrid Hierarchical Multi-Agent Framework (H²MAF), which fuses EfficientNet-B3 and ConvNeXt-Tiny with multimodal large language models Gemma 4 E4B and Qwen3.5 4B to provide explainable plant disease diagnoses. On the PlantDoc benchmark, Gemma raised accuracy from 63.9% to 68.5%, and on Cornell field datasets, accuracies reached 99.3% and 98.9%, though Gemma showed lower critical-risk error than Qwen, indicating calibration-dependent utility for robotic field decision support.

arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Multi-Agent Framework H$^{2}$MAF , combining decision-level fusion of EfficientNet-B3 and ConvNeXt-Tiny with semantic arbitration by open-weight multimodal large language models MLLMs , Gemma 4 E4B and Qwen3.5 4B, using structured JSON evidence to generate explainable diagnoses, risk levels, treatment urgency, and financial exposure. H$^{2}$MAF is evaluated on 14,364 images 1,370 test images across PlantDoc 2,922 images, 27 classes and two non-public, continuously captured Cornell robot-acquired field datasets: Stage 2 20 GB; 4,215 images and Stage 4 40 GB; 7,227 images , covering Early Blight, Late Blight, and Septoria Leaf Spot under uncontrolled field conditions. On PlantDoc, Gemma improves accuracy from 63.9% to 68.5%, achieving +7.6 points on the 41.7% CNN-conflict subset. Cornell accuracies reach 99.3% and 98.9%, with only 1.7-4.1% disagreement, demonstrating conflict-dependent MLLM utility. The critical-risk error of gemma is 0.14-0.5 points, whereas Qwen overflags by 3.5-14.4 points. These results establish MLLM arbitration as a promising, yet calibration-dependent, approach for explainable agricultural AI and robotic field decision support. Github Link: https://github.com/Applied-AI-Research-Lab/Explainable-AI-Plant-Disease-Detection