柑橘病虫害知识图谱构建研究

doi:10.16178/j.issn.0528-9017.20231218

浙江农业科学 ›› 2025, Vol. 66 ›› Issue (3): 687-691.DOI: 10.16178/j.issn.0528-9017.20231218

柑橘病虫害知识图谱构建研究

朱逸航¹^,²(), 张小敏¹^,², 周天奇³, 饶秀勤¹^,²^,^*()

1.浙江大学生物系统工程与食品科学学院,浙江杭州 310058
2.农业农村部农产品产地处理装备重点实验室, 浙江杭州 310058
3.浙江开浦科技有限公司,浙江海宁 314408

收稿日期:2023-12-21 出版日期:2025-03-11 发布日期:2025-04-02
通讯作者: 饶秀勤
作者简介:饶秀勤(1968—),男,湖北天门人,教授,博士,主要从事智能农业装备,E-mail:xqrao@zju.edu.com。
朱逸航(1998—),男,浙江杭州人,本科,主要从事智能农业装备,E-mail:yihang_zhu@zju.edu.cn。
基金资助:
国家重点研发计划项目(2023YFD1600300);:乡村产业共性关键技术研发与集成应用(乡村产业共性关键技术研发与集成应用)

Study on the construction of knowledge graph for diseases and pests of citrus

ZHU Yihang¹^,²(), ZHANG Xiaomin¹^,², ZHOU Tianqi³, RAO Xiuqin¹^,²^,^*()

1. College of Biosystem Engineering and Food Science, Zhejiang University, Hangzhou 310058,Zhejiang
2. Key Laboratory of Agricultural Products Processing Equipment, Ministry of Agriculture and Rural Affairs, Hangzhou 310058,Zhejiang
3. Zhejiang Kepler Technology Co., Ltd., Haining 314408,Zhejiang

Received:2023-12-21 Online:2025-03-11 Published:2025-04-02
Contact: RAO Xiuqin

摘要/Abstract

摘要：

在传统柑橘生产中,农户缺乏丰富的农业专业知识和技术。信息技术可以将柑橘病虫害防治知识数字化。然而,当前的知识管理技术在效率、可扩展性和适用性方面存在一些缺陷。知识图谱为知识管理提供了一种新的方法,它是一种更加灵活的知识管理方式。鉴于现有农业知识图谱对柑橘病虫害防治相关实体和关系的刻画不够详细,本研究对柑橘病虫害知识进行了实体类型和关系种类的定义,共划分出10种实体类别和10种实体关系,并利用大语言模型进行提取和去重,实现对16种常见缺陷的管理,使用Neo4j数据库对知识图谱进行了存储和可视化。最后着重探讨了柑橘病虫害知识图谱如何为精准病虫害信息查询、智能辅助诊断等下游任务提供底层技术支撑。

关键词: 柑橘病虫害, 知识图谱, Neo4j, 大语言模型

Abstract:

In traditional cultivation of citrus, farmers lack abundant professional agricultural knowledge and technology. Information technology can digitize the knowledge of pest and disease control of citrus. However, current knowledge management technologies have some common deficiencies in terms of efficiency, scalability, and applicability. Knowledge graph provides a new approach to knowledge management, which is a more flexible method. Considering the insufficient detailed characterization of entities and relationships related to pest and disease control of citrus in existing agricultural knowledge graphs, this study defines entity types and relationship categories for pest and disease knowledge of citrus. A total of 10 entity categories and 10 entity relationships were identified, and a large language model was used for extraction and deduplication, addressing 16 common deficiencies. Finally, the knowledge graph was stored and visualized using the Neo4j database. The discussion focused on how knowledge graph for pest and disease of citrus can provide underlying technical support for downstream tasks such as precise pest and disease information inquiry and intelligent-assisted diagnosis.

Key words: pest and disease of citrus, knowledge graph, Neo4j, large language model

中图分类号:

S436.66

朱逸航, 张小敏, 周天奇, 饶秀勤. 柑橘病虫害知识图谱构建研究[J]. 浙江农业科学, 2025, 66(3): 687-691.

ZHU Yihang, ZHANG Xiaomin, ZHOU Tianqi, RAO Xiuqin. Study on the construction of knowledge graph for diseases and pests of citrus[J]. Journal of Zhejiang Agricultural Sciences, 2025, 66(3): 687-691.

图/表 6

图1 知识图谱框架图

Fig.1 Framework diagram of knowledge graph construction

图2 图谱领域本体建模

Fig.2 Ontology modeling in the graph domain

表1 柑橘病虫害数据集

Table 1 Pest and disease dataset of citrus

关系类型	数量/个	实体类别	数量/个
分布	72	症状	176
预防	85	特征	283
治疗	99	地理位置	46
危害	46	果树位置	24
关注	18	规律特点	205
表现	223	时间	41
发生	233	预防方法	60
特征显示	308	治疗方法	84
暴发	48	病虫种类	3
防治	29	危害等级	9

图3 柑橘病虫害知识图谱可视化

Fig.3 Visualization of knowledge graph of citrus disease and pest

图4 病虫害信息查询

Fig.4 Information inquiry of pest and disease

图5 病虫害危害位置查询

Fig.5 Inquiry of the hazards location of pest and disease

参考文献 13

[1]	尹怀中, 傅中渝, 彭昌家, 等. 柑橘病虫害全程绿色防控技术集成试验示范效果[J]. 中国植保导刊, 2018, 38(3): 51-57.
[2]	刘杰帆, 林铭佳, 张建桃, 等. 基于CiteSpace可视化分析的柑橘黄龙病研究进展[J]. 果树学报, 2023, 40(4): 771-787.
[3]	邓明学. 以控制木虱为重点的柑橘黄龙病综合防治技术研究[J]. 植物保护, 2006, 32(6): 147-149.
[4]	WANG Y, WANG Y, WANG J, et al. An ontology-based approach to integration of hilly citrus production knowledge[J]. Computers and Electronics in Agriculture, 2015, 113: 24-43.
[5]	GUO M, ZHANG J Y, GENG N, et al. Chinese relation extraction of apple diseases and pests based on BERT and entity information[M]// Knowledge Science, Engineering and Management. Cham: Springer International Publishing, 2022: 579-592.
[6]	唐闻涛, 胡泽林. 农业知识图谱研究综述[J]. 计算机工程与应用, 2024, 60(2): 63-76.
[7]	吴赛赛, 周爱莲, 谢能付, 等. 基于深度学习的作物病虫害可视化知识图谱构建[J]. 农业工程学报, 2020, 36(24): 177-185.
[8]	张永亮. 基于知识图谱的苹果病虫害智能问答系统研究[D]. 杨凌: 西北农林科技大学, 2022.
[9]	闫丽华. 基于知识图谱的葡萄病虫害自动问答系统[D]. 杨凌: 西北农林科技大学, 2021.
[10]	陈亚东, 鲜国建, 寇远涛, 等. 我国苹果产业知识图谱构建研究[J]. 中国农业资源与区划, 2017, 38(11): 40-45.
[11]	王栋, 周菲, 李颖芳, 等. 我国甜樱桃产业知识图谱构建研究[J]. 中国果树, 2023(1): 104-108.
[12]	杨林楠, 郜鲁涛, 林尔升, 等. 基于Android系统手机的甜玉米病虫害智能诊断系统[J]. 农业工程学报, 2012, 28(18): 163-168.
[13]	VASSILIOU G, PAPADAKIS N, KONDYLAKIS H. SummaryGPT: leveraging ChatGPT for summarizing knowledge graphs[M]//The Semantic Web:ESWC 2023 Satellite Events. Cham: Springer Nature Switzerland, 2023: 164-168.

柑橘病虫害知识图谱构建研究

Study on the construction of knowledge graph for diseases and pests of citrus

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 13

相关文章 1

编辑推荐

Metrics

本文评价