Agri-CPJ: A Training-Free Explainable Framework for Agricultural Pest Diagnosis Using Caption-Prompt-Judge and LLM-as-a-Judge
Agri-CPJ:一种无需训练的可解释框架,用于利用Caption-Prompt-Judge和LLM-as-a-Judge进行农业害虫诊断
机构 * Business School, Shandong University of Technology(山东理工大学商学院) ; Faculty of Data Science, City University of Macau(澳门城市大学数据科学学院) ; Guangdong Institute of Intelligent Science and Technology(广东智能科学与技术研究院) ; Macau Millennium College(澳门 millennium 学院) ; Department of Computer and Information Science, University of Macau(澳门大学计算机与信息科学系)
专题命中 诊断辅助 :diagnosis(title,abstract);分类 cs.CV
AI总结 本文提出Agri-CPJ框架,通过生成结构化形态描述并利用LLM进行判断,解决农业病害诊断中模型易产生错误物种名称和推理不可用的问题,实验显示其在病害分类和问答评分上有显著提升。
Comments This work is an expanded version of our prior paper published in the IEEE ICASSP 2026 conference arXiv:2512.24947, from 4 to 20+ pages, presenting a well-structured and principled framework, extensive experiments, and deeper insights. Tao Fang is the corresponding author