农业机械学报2025，Vol.56Issue(1)：25-36,46,13.DOI:10.6041/j.issn.1000-1298.2025.01.003
基于电子病历多模态数据的作物病害多元场景处方推荐方法研究
Multi-scenario Prescription Recommendations for Crop Diseases Based on Multi-modal Data of Electronic Medical Records
摘要
Abstract
Considering challenges such as diverse crop varieties,complex disease types,significant sample data imbalance,varied prescription categories,and multi-modal data,prescription recommendation methods tailored to diverse,extensible,and multi-modal application scenarios were explored by using multi-modal EMR data.To accommodate the varying prescription preferences of agricultural producers,a diversified crop disease prescription recommendation model based on CdsBERT-RCNN and diagnostic reasoning was developed,improving diagnostic accuracy and prescription diversity for 32 common diseases.For untrained rare diseases and newly added prescriptions,an extensible crop disease prescription recommendation model based on MC-SEM and semantic retrieval was developed,enhancing semantic matching accuracy and case library retrieval speed,and providing prescription recommendations for untrained diseases.For multimodal information collection and input,a multi-modal crop disease prescription recommendation model based on BATNet multi-layer feature fusion was developed,enhancing prescription recommendation performance for multimodal data inputs.Results demonstrated that CdsBERT-RCNN achieved an 85.65％diagnostic accuracy and an Fl score of 85.63％across the 32 common diseases.In tests with varying input completeness levels,the model achieved 81.19％accuracy with symptom information alone,and the inclusion of environmental and crop information improved accuracy by 1.65 percentage points and 3.61 percentage points,respectively.MC-SEM achieved a Pearson correlation coefficient of 86.34％and a Spearman correlation coefficient of 77.67％for EMR semantic matching tasks;and achieved accuracy of 88.20％and 82.04％in the closed-set and open-set prescription recommendation tests,respectively,demonstrating its capability to expand to untrained diseases.BATNet achieved an accuracy and Fl score of 98.88％and 98.83％,respectively,for multi-modal input prescription recommendation tasks.Application scenario analysis and testing validated the model's generalization capability for incomplete modalities(pure text or pure image)and incomplete information input(crop,environment,symptoms).The research result would provide an idea for digitally enabled crop disease control decision-making.关键词
作物病害处方推荐/自然语言处理/语义检索/多模态融合/电子病历Key words
prescription recommendation for crop diseases/natural language processing/semantic retrieval/multi-modal fusion/electronic medical records分类
计算机与自动化引用本文复制引用
张领先,丁俊琦,陈菲菲,李宜滨,张一丁..基于电子病历多模态数据的作物病害多元场景处方推荐方法研究[J].农业机械学报,2025,56(1):25-36,46,13.基金项目
国家自然科学基金项目(62376272)和全国农业专业学位研究生教育指导委员会研究生教育研究重点课题(2021-NYZD-07) （62376272）
