无需微调，用大模型生成可解释的作物病害诊断结果
CPJ: Explainable Agricultural Pest Diagnosis via Caption-Prompt-Judge with LLM-Judged Refinement
- 用多角度图像描述+大模型评判迭代优化，实现无训练诊断
- 在CDDMBench上疾病识别准确率提升22.7个百分点，问答得分提高19.5
- 适合需要可解释性农业决策的科研与基层农技人员使用
精准且可解释的作物病害诊断对农业决策至关重要，但现有方法常依赖昂贵的监督微调，在领域迁移下表现不佳。我们提出Caption--Prompt--Judge（CPJ）框架，一种无需训练的少样本方法，通过结构化、可解释的图像描述增强农业病害视觉问答能力。CPJ利用大视觉语言模型生成多角度描述，经由大模型作为裁判模块迭代优化，再用于双答案视觉问答流程，实现病害识别与管理建议。在CDDMBench评测中，使用GPT-5-mini生成的描述，GPT-5-Nano在疾病分类上较无描述基线提升22.7个百分点，在问答得分上提升19.5分。该框架提供透明、基于证据的推理过程，实现了无需微调的鲁棒且可解释的农业诊断。代码与数据已公开于：https://github.com/CPJ-Agricultural/CPJ-Agricultural-Diagnosis。
原文摘要 · Abstract (English)
Accurate and interpretable crop disease diagnosis is essential for agricultural decision-making, yet existing methods often rely on costly supervised fine-tuning and perform poorly under domain shifts. We propose Caption--Prompt--Judge (CPJ), a training-free few-shot framework that enhances Agri-Pest VQA through structured, interpretable image captions. CPJ employs large vision-language models to generate multi-angle captions, refined iteratively via an LLM-as-Judge module, which then inform a dual-answer VQA process for both recognition and management responses. Evaluated on CDDMBench, CPJ significantly improves performance: using GPT-5-mini captions, GPT-5-Nano achieves \textbf{+22.7} pp in disease classification and \textbf{+19.5} points in QA score over no-caption baselines. The framework provides transparent, evidence-based reasoning, advancing robust and explainable agricultural diagnosis without fine-tuning. Our code and data are publicly available at: https://github.com/CPJ-Agricultural/CPJ-Agricultural-Diagnosis.
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