基于电子病历的作物病虫害关联挖掘及智能诊断  

Association mining of crop diseases and intelligent diagnosis based on electronic medical records

作　　者：徐畅 张领先[1] 乔岩 XU Chang;ZHANG Lingxian;QIAO Yan(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;Beijing Plant Protection Station,Beijing 100029,China)

机构地区：[1]中国农业大学信息与电气工程学院,北京市100083 [2]北京市植物保护站,北京市100029

出　　处：《智能化农业装备学报（中英文）》2023年第4期1-10,共10页Journal of Intelligent Agricultural Mechanization

基　　金：国家自然科学基金(62176261,62376272)。

摘　　要：作物病虫害研究是人工智能技术与智慧农业交叉领域的热点问题。现有的研究受到数据获取困难、技术实施成本高以及作物病虫害发生态势复杂等因素的限制。北京市“植物诊所”形成的植物电子病历(plant electronic medical records,PEMRs)为作物病虫害的诊断与防治提供了新的研究方向。PEMRs以多模态数据的形式存储,包含了丰富的植物信息、病虫害信息和环境信息,如何挖掘PEMRs信息并利用其辅助后续研究是亟待解决的问题。鉴于知识图谱的信息表示能力、机器学习的挖掘能力和深度学习的特征抽取能力,根据电子病历特点,利用结构化数据构建作物病虫害知识图谱,利用非结构化数据和领域知识进行知识增强,进一步利用Neo4j图数据库和图数据科学(graph data science,GDS)结合机器学习算法从“热”点发现、联系链路发现、相似病虫害发现3个维度进行关联挖掘。在此基础上,将基于Transformer的双向编码器(bidirectional encoder representation from transformers,BERT)与卷积神经网络(convolutional neural network,CNN)结合,利用非结构化文本数据实现文本特征抽取和病虫害诊断,模拟植物医生实现智能化服务,在20种常见病虫害上的综合准确率可达到93.13%。本研究可为作物病虫害的及时诊断、对症防治、科学用药和辅助决策提供理论支持,创新了农业科技社会化服务新模式、新业态。Research on crop diseases has become a hot topic of the application of artificial intelligence technology in smart agriculture.Existing studies are restricted by the factors such as difficulties in collecting data,high cost of technology implementation,and complex crop disease trends.Plant electronic medical records(PEMRs)formed by Beijing Plant Clinic provides a new idea for the diagnosis and prevention of crop diseases and pests.PEMRs are stored in the form of multi-modal data,containing a wealth of plant information,disease information,and environmental information.Therefore,how to mine PEMRs information and utilize it to assist follow-up research is an urgent problem to be solved.In view of the information representation ability of knowledge map,the mining ability of machine learning and the feature extraction ability of deep learning,structured data is used to construct the knowledge graph of crop diseases and pests,unstructured data and domain knowledge are used for knowledge enhancement according to the characteristics of PEMRs.Further,Neo4j graph database and graph data science(GDS)combined with machine learning algorithm was employed to conduct association mining from three dimensions of popular point discovery,link discovery and similar disease discovery.At the same time,text feature extraction and disease diagnosis were realized by using unstructured text data based on the bidirectional encoder representation from transformers(BERT)with convolutional neural network(CNN)model,and intelligent service was realized by simulating plant doctor.The comprehensive accuracy of 20 common diseases was up to 93.13%.This study can provide theoretical support for timely diagnosis,symptomatic control,scientific medication guide and assistant decision-making of crop diseases and pests,and innovate a new model and new business of social service of agricultural science and technology.

关 键 词：作物病虫害 知识图谱 关联挖掘 植物电子病历 病虫害诊断 机器学习 

分 类 号：S24[农业科学—农业电气化与自动化] TP391[农业科学—农业工程]