Survey of Agricultural Knowledge Graph

doi:10.3778/j.issn.1002-8331.2305-0203

Abstract

Abstract: Knowledge graphs are a key technology in the era of big data, specifically for knowledge engineering. Utilizing the powerful semantic understanding and knowledge organization capabilities of knowledge graphs, issues such as scattered and disordered agricultural knowledge, and insufficient coverage of knowledge in the construction of modern agriculture can be resolved. Firstly, considering the complexity and specialty of agricultural data, the construction methods and framework of agricultural knowledge graphs are introduced. Secondly, the current domestic and international research status of the four key technologies in the construction of agricultural knowledge graphs-ontology construction, knowledge extraction, knowledge fusion, and knowledge reasoning are reviewed. Furthermore, the systematic applications of agricultural knowledge graphs in decision support, intelligent question-answering systems, and recommendation systems are sorted out. Lastly, several specific instances of agricultural knowledge graphs are presented. Based on the current status of research on agricultural knowledge graphs, prospects for its future research directions are offered.

Key words: agriculture knowledge graph, ontology, knowledge extraction, knowledge fusion, knowledge reasoning

摘要： 知识图谱是大数据时代下知识工程的关键技术。利用知识图谱强大的语义理解和知识组织能力，可以解决现代化农业建设中农业知识分散无序、知识覆盖范围不足等问题针对农业领域数据复杂、专业性强等特点，给出了农业知识图谱的构建方法与框架；综述了农业知识图谱构建中本体构建、知识抽取、知识融合以及知识推理四个关键技术的国内外研究现状；系统梳理了农业知识图谱在决策支持、智能问答与推荐系统的应用；最后，介绍了几个具体的农业知识图谱实例。根据农业知识图谱的研究现状，对其未来的研究方向进行了展望。

关键词: 农业知识图谱, 本体, 知识抽取, 知识融合, 知识推理

TANG Wentao, HU Zelin. Survey of Agricultural Knowledge Graph[J]. Computer Engineering and Applications, 2024, 60(2): 63-76.

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.

References

[1] SHETH A, THIRUNARAYAN K. Semantics empowered web 3.0: managing enterprise, social, sensor, and clound-based data and services for advanced applications[M]. San Rafael, CA: Morgan & Claypool Publishers, 2013.
[2] SHADBOLT N, BERNERS L T, HALL W. The semantic web revisited[J]. IEEE Intelligent Systems, 2006, 21(3): 96-101.
[3] SINGHAL A. Introducing the knowledge graph: things, not strings[EB/OL]. (2012-05-16)[2023-06-05]. https://blog.google/products/search/introducing-knowledge-graph-things-not/.
[4] BOLLACKER K, EVANS C, PARITOSH P, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data, 2008: 1247-1250.
[5] SUCHANEK F M, KASNECI G, WEIKUM G. Yago: a large ontology from wikipedia and wordnet[J]. Journal of Web Semantics, 2008, 6(3): 203-217.
[6] NIU X, SUN X R, WANG H F, et al. Zhishi. me-weaving Chinese linking open data[C]//Proceedings of the 10th International Semantic Web Conference, 2011: 205-220.
[7] XU B, LIANG J, XIE C, et al. CN-dbpedia2: an extraction and verification framework for enriching chinese encyclopedia knowledge base[J]. Data Intelligence, 2019, 1(3): 244-261.
[8] 蒋川宇, 韩翔宇, 杨文蕊, 等. 医学知识图谱研究与应用综述[J]. 计算机科学, 2023, 50(3): 83-93.
JIANG Y C, HAN X Y, YANG W R, et al. Survey of medical knowledge graph research and application[J]. Computer Science, 2023, 50(3): 83-93.
[9] 范媛媛, 李忠民. 中文医学知识图谱研究及应用进展[J]. 计算机科学与探索, 2022, 16(10): 2219-2233.
FAN Y Y, LI Z M. Research and application progress of chinese medical knowledge graph[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2219-2233.
[10] 江旭晖, 沈英汉, 李子健, 等. 社交知识图谱研究综述[J]. 计算机学报, 2023, 46(2): 304-330.
JIANG X H, SHEN Y H, LI Z J, et al. A survey of social knowledge graph[J]. Chinese Journal of Computers, 2023, 46(2): 304-330.
[11] 邱凌, 张安思, 李少波, 等. 航空制造知识图谱构建研究综述[J]. 计算机应用研究, 2022, 39(4): 968-977.
QIU L, ZHANG A S, LI S B, et al. Survey on building knowledge graphs for aerospace manufacturing[J]. Application Research of Computers, 2022, 39(4): 968-977.
[12] 邱云飞, 邢浩然, 李刚. 矿井建设知识图谱构建研究综述[J]. 计算机工程与应用, 2023, 59(7): 64-79.
QIU Y F, XING H R, LI G. Summary of research on the construction of knowledge graph for mine construction[J]. Computer Engineering and Applications, 2023, 59(7): 64-79.
[13] LIU X X, BAI X S, WANG L H, et al. Review and trend analysis of knowledge graphs for crop pest and diseases[J]. IEEE Access, 2019, 7: 62251-62264.
[14] MIN W Q, LIU C L, XU L Y, et al. Applications of knowledge graphs for food science and industry[J]. Patterns, 2022, 3(5): 100484.
[15] MOL E A N, KUMAR M B S. Review on knowledge extraction from text and scope in agriculture domain[J]. Artificial Intelligence Review, 2022, 56(5): 4403-4445.
[16] QIAO B, FANG K, CHEN Y M, et al. Building thesaurus-based knowledge graph based on schema layer[J]. Cluster Computing, 2017, 20(1): 81-91.
[17] 赵宇博, 张丽萍, 闫盛, 等. 个性化学习中学科知识图谱构建与应用综述[J]. 计算机工程与应用, 2023, 59(10): 1-21.
ZHAO Y B, ZHANG L P, YAN S, et al. Construction and application of discipline knowledge graph in personalized learning[J]. Computer Engineering and Applications, 2023, 59(10): 1-21.
[18] 魏圆圆. 基于本体论的农业知识建模及推理研究[D]. 合肥: 中国科学技术大学, 2011.
WEI Y Y. Research of ontology-based agricultural knowledge modeling and reasoning[D]. Hefei: University of Science and Technology of China, 2011.
[19] 李贯峰, 李卫军. 一个基于枸杞病虫害领域本体的语义检索模型[J]. 计算机技术与发展, 2017, 27(9): 48-52.
LI G F, LI W J. A semantic retrieval model with domain ontology based on wolfberry disease and pets[J]. Computer Technology and Development, 2017, 27(9): 48-52.
[20] 许多, 鲁旺平, 许瑞清, 等. 基于农业时空多模态知识图谱的水稻精准施肥决策方法[J]. 华中农业大学学报, 2023, 42(3): 281-292.
XU D, LU W P, XU R Q, et al. A method of deciding the precision fertilization of rice based on the spatio-temporal multi-modal knowledge graph of agriculture[J]. Journal of Huazhong Agricultural University, 2023, 42(3): 281-292.
[21] DEEPA R, VIGNESHWARI S. An effective automated ontology construction based onthe agriculture domain[J]. ETRI Journal, 2022, 44(4): 573-587.
[22] SAAT N L Y, NOAH S M. Rule-based approach for automatic ontology population of agriculture domain[J]. Inform Technology, 2016, 15(2): 46-51.
[23] 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.
[24] 刘乾凝. 面向数字人文的都市农业本体的构建[J]. 图书馆杂志, 2019, 38(8): 53-58.
LIU Q N. Construction of urban agriculture ontology oriented to digital humanities[J]. Library Journal, 2019, 38(8): 53-58.
[25] 刘桂锋, 杨倩, 刘琼. 农业科学数据集的本体构建与可视化研究—以“棉花病害防治”领域为例[J]. 情报杂志, 2022, 41(9): 143-149.
LIU G F, YANG Q, LIU Q. Ontology construction and visualization display of agricultural science data set-taking the field of “Cotton Disease Control” as an example[J]. Journal of Intelligence, 2022, 41(9): 143-149.
[26] 王昊奋, 漆桂林, 陈华钧. 知识图谱: 方法、实践与应用[M]. 北京: 电子工业出版社, 2019.
WANG H F, GUI Q L, CHEN H J. Knowledge graph: method, practice and application[M]. Beijing: Publishing House of Electronics Industry, 2019.
[27] 田玲, 张谨川, 张晋豪, 等. 知识图谱综述——表示、构建、推理与知识超图理论[J]. 计算机应用, 2021, 41(8): 2161-2186.
TIAN L, ZHANG J C, ZHANG J H, et al. Knowledge graph survey: representation, construction, reasoning and knowledge hypergraph theory[J]. Journal of Computer Applications, 2021, 41(8): 2161-2186.
[28] EFTIMOV T, SELJAK B K, KOROSEC P. A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations[J]. PLoS One, 2017, 12(6): e0179488.
[29] CHATTERJEE N, KAUSHIK N. RENT: regular expression and NLP-based term extraction scheme for agricultural domain[C]//Proceedings of the International Conference on Data Engineering and Communication Technology, 2017: 511-522.
[30] ZHOU G D. Recognizing names in biomedical texts using mutual information indepenence model and SVM plus sigmoid[J]. International Journal of Medical Informatics, 2006, 75(6): 456-467.
[31] ZHANG J, SHEN D, ZHOU G D, et al. Enhancing HMM-based biomedical named entity recognition by studying special phenomena[J]. Journal of Biomedical Informatics, 2004, 37(6): 411-422.
[32] HAO Z F, WANG H F, CAI R C, et al. Product named entity recognition for Chinese query questions based on a skip-chain CRF model[J]. Neural Computing and Applications, 2013, 23(2): 371-379.
[33] 王春雨, 王芳. 基于条件随机场的农业命名实体识别研究[J]. 河北农业大学学报, 2014, 37(1): 132-135.
WANG C Y, WANG F. Study on recognition of chinese agricultural named entity with conditional random fields[J]. Journal of Agricultural University of Hebei, 2014, 37(1): 132-135.
[34] 李想, 魏小红, 贾璐, 等. 基于条件随机场的农作物病虫害及农药命名实体识别[J]. 农业机械学报, 2017, 48(S1): 178-185.
LI X, WEI X H, JIA L, et al. Recognition of crops, diseases and pesticides named entities in chinese based on conditional random fields[J]. Transactions of the Chinese Society of Agricultural Machinery, 2017, 48(S1): 178-185.
[35] LAMPLE G, BALLESTEROS M, SUBRAMANIAN S, et al. Neural architectures for named entity recognition[C]//Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics, 2016: 260-270.
[36] 宋林鹏, 刘世洪, 王翠. 基于词向量+BiLSTM+CRF的农业技术需求文本实体提取[J]. 江苏农业科学, 2021, 49(5): 186-193.
SONG L P, LIU S H, WANG C. Extraction of text entity of agricultural technology demand based on Word Vector+BiLSTM+CRF[J]. Jiangsu Agricultural Sciences, 2021, 49(5): 186-193.
[37] 赵鹏飞, 赵春江, 吴华瑞, 等. 基于注意力机制的农业文本命名实体识别[J]. 农业机械学报, 2021, 52(1): 185-192.
ZHAO P F, ZHAO C J, WU H R, et al. Named entity recognition of chinese agricultural text based on attention mechanism[J]. Transactions of the Chinese Society of Agricultural Machinery, 2021, 52(1): 185-192.
[38] GUO X C, HAN Z, JIE S, et al. Chinese agricultural diseases and pests named entity recognition with multi-scale local context features and self-attention mechanism[J]. Computers and Electronics in Agriculture, 2020, 179: 105830.
[39] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of Conference of the North American Chapter of the Association for Computational Linguistic, 2019: 4171-4186.
[40] MENG F Q, YANG S S, WANG J D, et al. Creating knowledge graph of electric power equipment faults based on BERT-BiLSTM-CRF model[J]. Journal of Electrical Engineering & Technology, 2022, 17(4): 2507-2516.
[41] ZHAO P F, WANG W, LIU H, et al. Recognition of the agricultural named entities with multifeature fusion based on ALBERT[J]. IEEE Access, 2022, 10: 98936-98943.
[42] 韦紫君, 宋玲, 胡小春, 等. 基于实体级遮蔽BERT与BiLSTM-CRF的农业命名实体识别[J]. 农业工程学报, 2022, 38(15): 195-203.
WEI Z J, SONG L, HU X C, et al. Named entity recognition of agricultural based entity-level masking BERT and BiLSTM-CRF[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(15): 195-203.
[43] LIU Z H, CHEN Y Y, DAI Y F, et al. Syntactic and semantic features based relation extraction in agriculture domain[C]//Proceedings of International Conference on Web Information Systems and Applications, 2018: 252-258
[44] 吴粤敏, 丁港归, 胡滨. 基于注意力机制的农业金融文本关系抽取研究[J]. 数据分析与知识发现, 2019, 3(5): 86-92.
WU Y M, DING G G, HU B. Extracting relationship of agricultural financial texts with attention mechanism[J]. Data Analysis and Knowledge Discovery, 2019, 3(5): 86-92.
[45] MINTZ M, BILLS S , SNOW R, et al. Distant supervision for relation extraction without labeled data[C]//Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2009: 1003-1011.
[46] ZENG D, LIU K, LAI S, et al. Relation classification via convolutional deep neural network[C]//Proceedings of the 25th International Conference on Computational Linguistics, 2014: 2335-2344.
[47] 乐毅, 王文宇, 张凯, 等. 基于多层注意力机制的农业病虫害远程监督关系抽取研究[J]. 安徽农业大学学报, 2020, 47(4): 682-686.
YUE Y, WANG W Y, ZHANG K, et al. Agricultural pest and disease relation extraction based on multi-attention mechanism and distant supervision[J]. Journal of Anhui Agricultural University, 2020, 47(4): 682-686.
[48] 唐璐. 基于知识抽取的徽茶知识图谱构建与应用[D]. 合肥: 安徽农业大学, 2022.
TANG L. Construction and application of knowledge graph for anhui tea based on knowledge extraction[D]. Hefei: Anhui Agricultural University, 2022.
[49] 董哲, 王亚, 马传孝, 等. 融合对抗训练和胶囊网络的食品安全关系抽取模型[J]. 科学技术与工程, 2022, 22(23): 10162-10168.
DONG Z, WANG Y, MA C X, et al. Food safety relation extraction model based on adversarial training and capsule network[J]. Science Technology and Engineering, 2022, 22(23): 10162-10168.
[50] QIAO B, ZOU Z Y, HUANG Y, et al. A joint model for entity and relation extraction based on BERT[J]. Neural Computing and Applications, 2021, 34(5): 3471-3481.
[51] 沈利言, 姜海燕, 胡滨, 等. 水稻病虫草害与药剂实体关系联合抽取算法[J]. 南京农业大学学报, 2020, 43(6): 1151-1161.
SHEN L Y, JIANG H Y, HU B, et al. A study on joint entity recognition and relation extraction for rice diseases pests weeds and drugs[J]. Journal of Nanjing Agricultural University, 2020, 43(6): 1151-1161.
[52] 胡滨, 汤保虎, 姜海燕, 等. 家禽诊疗文本多实体关系联合抽取模型研究[J]. 农业机械学报, 2021, 52(6): 268-276.
HU B, TANG B H, JIANG H Y, et al. Joint extraction model of multi-entity elations for poultry diagnosis and treatment text[J]. Transactions of the Chinese Society of Agricultural Machinery, 2021, 52(6): 268-276.
[53] 吴赛赛, 梁晓贺, 谢能付, 等. 面向领域实体关系联合抽取的标注方法[J]. 计算机应用, 2021, 41(10): 2858-2863.
WU S S, LIANG X X, XIE N F, et al. Annotation method for joint extraction of domain-oriented entities and relations[J]. Journal of Computer Applications, 2021, 41(10): 2858-2863.
[54] 赵瑞雪, 杨晨雪, 郑建华, 等. 农业智能知识服务研究现状及展望[J]. 智慧农业, 2022, 4(4): 105-125.
ZHAO R X, YANG C X, ZHENG J H, et al. Agricultural intelligent knowledge service: overview and future perspectives[J]. Smart Agriculture, 2022, 4(4): 105-125.
[55] 谢能付. 基于农业本体和融合规则的知识融合框架研究[J]. 安徽农业科学, 2013, 41(1): 395-397.
XIE N F. Knowledge fusion framework based on agricultural ontology and fusion rules[J]. Journal of Anhui Agricultural Sciences, 2013, 41(1): 395-397.
[56] SUAREZ F M C, GOMEZ P A, FERNANDEZ L M. The NeOn methodology framework: a scenario-based methodology for ontology development[J]. Applied Ontology, 2015, 10(2): 107-145.
[57] MULJARTO A R, SALMON J M, CHARNOMORDIC B, et al. A generic ontological network for Agri-food experiment integration-application to viticulture and winemaking[J]. Computers and Electronics in Agriculture, 2017, 140: 433-442.
[58] ZHANG L Y, REN J D, LI X W. OIM-SM: a method for ontology integration based on semantic mapping[J]. Journal of Intelligent & Fuzzy Systems, 2017, 32(3): 1983-1995.
[59] 贾丙静. 基于表示学习的实体识别和链接关键技术研究[D]. 北京: 北京邮电大学, 2021.
JIA B J. Research on key technologies of named entity recognition and linking based on representation learning[D]. Beijing: Beijing University of Posts and Telecommunications, 2021.
[60] HUANG H, Heck L, JI H. Leveraging deep neural networks and knowledge graphs for entity disambiguation[J]. arXiv:1504.07678, 2015.
[61] 夏迎春. 基于知识图谱的农业知识服务系统研究[D]. 合肥: 安徽农业大学, 2018.
XIA Y C. Agriculture knowledge service system based on knowledge graph[D]. Hefei: Anhui Agricultural University, 2018.
[62] 杨洁. 基于本体的柑橘病虫害知识建模及推理研究[D]. 武汉: 华中师范大学, 2014.
YANG J. Research on knowledge modeling and reasoning ontology-based of citrus disease and pests[D]. Wuhan: Central China Normal University, 2014.
[63] 牟向伟, 陈燕, 曹妍. 农产品冷链 HACCP 管理体系知识建模与推理[J]. 农业工程学报, 2016, 32(2): 300-308.
MU X W, CHEN Y, CAO Y. HACCP knowledge modeling and reasoning for agricultural products cold-chain logistics[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(2): 300-308.
[64] 戈为溪, 周俊, 袁立存, 等. 基于知识图谱与案例推理的水稻精准施肥推荐模型[J]. 农业工程学报, 2023, 39(2): 126-133.
GE W X, ZHOU J, YUAN L C, et al. Recommendation model for rice precision fertilization using knowledge graph and case-based reasoning[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(2): 126-133.
[65] 吴安捷. 基于PPO强化学习算法的农业领域知识图谱推理方法研究[D]. 合肥: 安徽农业大学, 2021.
WU A J. Research on the reasoning method of agricultural knowledge graph based on proximal policy optimization[D]. Hefei: Anhui Agricultural University, 2021.
[66] 王亦斌, 孙涛, 梁雪春, 等. 基于EMD-LSTM模型的河流水量水位预测[J]. 水利水电科技进展, 2020, 40(6): 40-47.
WANG Y B, SUN T, LIANG X C, et al. Prediction of river water flow and water level based on EMD-LSTM model[J]. Advances in Science and Technology of Water Resources, 2020, 40(6): 40-47.
[67] 王献锋, 张传雷, 张善文, 等. 基于自适应判别深度置信网络的棉花病虫害预测[J]. 农业工程学报, 2018, 34(14): 157-164.
WANG X F, ZHANG C L, ZHANG S W, et al. Forecasting of cotton diseases and pests based on adaptive discriminant deep belief network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(14): 157-164.
[68] 张善文, 王振, 王祖良. 结合知识图谱与双向长短时记忆网络的小麦条锈病预测[J]. 农业工程学报, 2020, 36(12): 172-178.
ZHANG S W, WANG Z, WANG Z L. Prediction of wheat stripe rust disease by combining knowledge graph and bidirectional long short term memory network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(12): 172-178.
[69] YAN W J, ZHANG Z S, ZHANG Q C, et al. Deep data analysis-based agricultural products management for smart public healthcare[J]. Frontiers in Public Health, 2022, 10: 847252.
[70] LI P P, HAO H J, ZHANG Z, et al. A field study to estimate heavy metal concentrations in a soil-rice system: Application of graph neural networks[J]. Science of the total environment, 2022, 832: 155099.
[71] 王萌, 王昊奋, 李博涵, 等. 新一代知识图谱关键技术综述[J]. 计算机研究与发展, 2022, 59(9): 1947-1965.
WANG M, WANG H F, LI B H, et al. Survey on key technology of new generation knowledge graph[J]. Journal of Computer Research and Development, 2022, 59(9): 1947-1965.
[72] 王艺, 王英, 原野, 等. 基于语义本体的柑橘肥水管理决策支持系统[J]. 农业工程学报2014, 30(9): 93-101.
WANG Y, WANG Y, YUAN Y, et al. A decision support system for fertilization and irrigation management of cit-rus based on semantic ontology[J]. Transactions of the Chinese Society of Agricultural Engineering 2014, 30(9): 93-101.
[73] HE J L, WANG J L, HE D X, et al. The design and implementation of an integrated optimal fertilization decision support system[J]. Mathematical and Computer Modelling, 2010, 54(3): 1167-1174.
[74] 于合龙, 沈金梦, 毕春光, 等. 基于知识图谱的水稻病虫害智能诊断系统[J]. 华南农业大学学报, 2021, 42(5): 105-116.
YU H L, SHEN J M, BI C G, et al. Intelligent diagnostic system for rice diseases and pests based on knowledge graph[J]. Journal of South China Agricultural University, 2021, 42(5): 105-116.
[75] WANG H D, SHEN W Z, ZHANG Y, et al. Diagnosis of dairy cow diseases by knowledge-driven deep learning based on the text reports of illness state[J]. Computers and Electronics in Agriculture, 2023, 205: 107564.
[76] 肖乐, 李家馨, 葛亮, 等. 面向粮情决策支持的知识图谱构建研究[J]. 中国粮油学报, 2022, 37(10): 29-37.
XIAO L, LI J X, GE L, et al. Knowledge graph construction for decision support of grain situation[J]. Journal of the Chinese Cereals and Oils Association, 2022, 37(10): 29-37.
[77] 史运涛, 刘召, 李书钦, 等. 基于知识图谱注意力网络的食品安全风险评估模型[J]. 食品工业, 2021, 42(12): 471-475.
SHI Y T, LIU Z, LI S Q, et al. Food Safety risk assessment model based on knowledge graph attention network[J]. The Food Industry, 2021, 42(12): 471-475.
[78] 张朝正, 陈婷, 潘登, 等. 冬奥会食品供应链有害因子知识图谱和智能化快筛技术研究[J]. 中国食品卫生杂志, 2022, 34(5): 884-888.
ZHANG C Z, CHEN T, PAN D, et al. Knowledge map and intelligent rapid screening technology of harmful factors in food supply chain of Winter Olympics[J]. Chinese Journal of Food Hygiene, 2022, 34(5): 884-888.
[79] XIA Y C, SUN N, WANG H, et al. Research on knowledge question answering system for agriculture disease and pets based on knowledge graph[J]. Journal of Nonlinear and Convex Analysis, 2020, 21(7): 1487-1496.
[80] WANG H R Q, ZHU H J, WU H R, et al. A densely con-nected GRU neural network based on coattention mechanism for chinese rice-related question similarity matching[J]. Agronomy, 2021, 11(7): 1307.
[81] 王俊. 基于知识图谱的饮食健康知识问答系统[D]. 南昌: 南昌大学, 2022.
WANG J. Diet and health knowledge question answering system based on knowledge graph[D]. Nanchang: Nanchang University, 2022.
[82] LAN Y B, GUO Y Q, CHEN Q Z, et al. Visual question answering model for fruit tree disease decision-making based on multimodal deep learning[J]. Frontiers in Plant Science, 2023, 13: 1064399.
[83] 孙琳. 基于知识图谱的农业在线信息资源推荐系统研究[D]. 长春: 吉林农业大学, 2021.
SUN L. Research on agricultural online information resource recommendation system based on knowledge graph[D]. Changchun: Jilin Agricultural University, 2021.
[84] 唐柳. 基于知识图谱的个性化农业新闻推荐系统研究[D]. 合肥: 安徽农业大学, 2022.
TANG L. Research on personalized agricultural news recommendation system based on knowledge graph[D]. Hefei: Anhui Agricultural University, 2022.
[85] ZOU Y D, PAN S H, YANG F, et al. Precise recommendation method of suitable planting areas of maize varieties based on knowledge graph[J]. Agriculture, 2023, 13(3): 526.
[86] CHEN Y, KUANG J, CHENG D, et al. AgriKG: an agricultural knowledge graph and its applications[C]//Database Systems for Advanced Applications, 2019: 533-537.
[87] JIN Y C, LIU J Z, WANG X H, et al. Technology recommendations for an innovative agricultural robot design based on technology knowledge graphs[J]. Processes, 2021, 9(11): 1905.
[88] LEI Z, HAQ A U, ZEB A, et al. Is the suggested food your desired?: multi-modal recipe recommendation with demand-based knowledge graph[J]. Expert Systems with Applications, 2021, 186(6): 115708.
[89] 张嘉宇, 郭玫, 张永亮, 等. 细粒度苹果病虫害知识图谱构建研究[J]. 计算机工程与应用, 2023, 59(5): 270-280.
ZHANG J Y, GUO M, ZHANG Y L, et al. Research on construction of fine-grained knowledge graph of apple diseases and pests[J]. Computer Engineering and Applications, 2023, 59(5): 270-280.
[90] 陈明, 朱珏樟, 席晓桃. 基于知识图谱的花卉病虫害知识管理方法[J]. 农业机械学报, 2023, 54(3): 291-300.
CHEN M, ZHU J Z, XI X T. Knowledge management method of flower diseases and pests based on knowledge graph[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(3): 291-300.
[91] 胡浩, 高静, 刘振羽. 奶牛产奶量性状相关基因知识图谱的研究与构建[J]. 计算机工程与应用, 2023, 59(2): 299-305.
HU H, GAO J, LIU Z Y. Research and construction of genetic knowledge graph of milk yield traits in dairy cows[J]. Computer Engineering and Applications, 2023, 59(2): 299-305.