Intelligent diagnostic system for rice diseases and pests based on knowledge graph

YU Helong; SHEN Jinmeng; BI Chunguang; LIANG Jie; CHEN Huiling

doi:10.7671/j.issn.1001-411X.202101010

Journal of South China Agricultural University > 2021 > 42(5): 105-116. > DOI: 10.7671/j.issn.1001-411X.202101010

YU Helong, SHEN Jinmeng, BI Chunguang, 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. DOI: 10.7671/j.issn.1001-411X.202101010

Citation:

PDF (15835 KB)

Intelligent diagnostic system for rice diseases and pests based on knowledge graph

YU Helong^{1, 2,},
SHEN Jinmeng^1,,
BI Chunguang^{1, 2},
LIANG Jie³,
CHEN Huiling^4, ,

1.
College of Information Technology, Jilin Agricultural University, Changchun 130118, China
2.
Institute of Smart Agriculture, Jilin Agricultural University, Changchun 130118, China
3.
College of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia
4.
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China

More Information

Received Date: January 05, 2021
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To conduct structured storage of complex and heterogeneous data information in the field of rice diseases and pests using knowledge graphs, establish semantic relationships between diseases and pests, and provide a theoretical basis for rice diseases and pests association retrieval and intelligent diagnosis.

Method
Firstly, a method of constructing a knowledge graph for rice diseases and pests was proposed. At the same time, a series of graph-based retrieval algorithms for rice diseases and pests were proposed for information mining, through introducing solar terms entities to achieve early warning of rice diseases and pests. Secondly, a knowledge reasoning method based on the combination of certainty factor (CF) model and knowledge graph was proposed to realize the intelligent diagnosis of rice diseases and pests by combining CF with the symptom of diseased plant.

Result
The accuracy rates of named entity recognition model were 0.92, 0.90, and 0.87 in disease and pest name and hazard symptom entities. Further, a knowledge graph of rice disease and pest domain including 1 972 entities and 5 226 entity relationships was constructed. Through the self-developed intelligent diagnosis system, case analysis was conducted and the test showed that the correct rate of the diagnosis algorithm reached 86.25%.

Conclusion
This study effectively solves the complexity and uncertainty of knowledge in data retrieval, early warning and diagnosis in the field of rice diseases and pests, and has a strong practical value and extension prospects.
- knowledge graph,
- certainty factor model,
- rice disease and pest,
- intelligent diagnosis

FullText(HTML)

References (37)

References

[1]	刘万才, 陆明红, 黄冲, 等. 水稻重大病虫害跨境跨区域监测预警体系的构建与应用[J]. 植物保护, 2020, 46(1): 87-92.
[2]	刘明辉, 沈佐锐, 高灵旺, 等. 基于WebGIS的农业病虫害预测预报专家系统[J]. 农业机械学报, 2009, 40(7): 180-186.
[3]	HU X F, CHENG C, LUO F, et al. Effects of different fertilization practices on the incidence of rice pests and diseases: A three-year case study in Shanghai, in subtropical southeastern China[J]. Field Crops Research, 2016, 196: 33-50. doi: 10.1016/j.fcr.2016.06.004
[4]	许童羽, 郭忠辉, 于丰华, 等. 采用GA-ELM的寒地水稻缺氮量诊断方法[J]. 农业工程学报, 2020, 36(2): 209-218. doi: 10.11975/j.issn.1002-6819.2020.02.025
[5]	孙娟, 蔡银杰, 冯成玉, 等. 无人机施药防治水稻病虫害参数组合初选[J]. 中国植保导刊, 2018, 38(12): 72-73. doi: 10.3969/j.issn.1672-6820.2018.12.016
[6]	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. doi: 10.3969/j.issn.1002-6819.2014.09.012
[7]	姚青, 张超, 王正, 等. 分布式移动农业病虫害图像采集与诊断系统设计与试验[J]. 农业工程学报, 2017, 33(S1): 184-191.
[8]	戴建国, 赖军臣. 基于图像规则与Android手机的棉花病虫害诊断系统[J]. 农业机械学报, 2015, 46(1): 35-44. doi: 10.6041/j.issn.1000-1298.2015.01.006
[9]	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. doi: 10.3969/j.issn.1002-6819.2012.18.024
[10]	马浚诚, 温皓杰, 李鑫星, 等. 基于图像处理的温室黄瓜霜霉病诊断系统[J]. 农业机械学报, 2017, 48(2): 195-202. doi: 10.6041/j.issn.1000-1298.2017.02.026
[11]	温皓杰, 张领先, 傅泽田, 等. 基于Web的黄瓜病害诊断系统设计[J]. 农业机械学报, 2010, 41(12): 178-182. doi: 10.3969/j.issn.1000-1298.2010.12.037
[12]	SARMA S K, SINGH K R, ABHIJEET S. An expert system for diagnosis of disease in rice plant[J]. International Journal of Artificial Intelligence, 2010, 1(2): 26-31.
[13]	HONGGOWIBOWO A S. A web-based rice plant expert system using rule-based reasoning[J]. Telkomnika, 2009, 7(3): 187-194. doi: 10.12928/telkomnika.v7i3.593
[14]	SHARMA R, CHANDERMOHAN, SINGH H, et al. Development of an image based expert system for identification of rice diseases and their management[J]. Plant Disease Research, 2012, 27(2): 158-161.
[15]	MUHIBUDDIN A, AIRLANGGA P, SULTHONI M M, et al. Implementing backward chaining method in expert system to detect and treat rice, chilli, and corn plant's pests and diseases[J]. Journal of Information Technology and Computer Engineering, 2018, 2(2): 71-75.
[16]	王昊奋, 丁军, 胡芳槐, 等. 大规模企业级知识图谱实践综述[J]. 计算机工程, 2020, 46(7): 1-13.
[17]	SU X L, LI J, CUI Y P, et al. Review on the work of agriculture ontology research group[J]. Journal of Integrative Agriculture, 2012, 11(5): 720-730. doi: 10.1016/S2095-3119(12)60061-6
[18]	索俊锋, 刘勇. 基于农业本体的语义相似度算法及其在农作物本体中的应用[J]. 农业工程学报, 2016, 32(16): 175-182. doi: 10.11975/j.issn.1002-6819.2016.16.024
[19]	DRURY B, FERNANDES R, MOURA M F, et al. A survey of semantic web technology for agriculture[J]. Information Processing in Agriculture, 2019, 6(4): 487-501.
[20]	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.
[21]	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. doi: 10.1109/ACCESS.2019.2915987
[22]	王娟. 基于案例推理和KG的烟草病害防控模型研究[D]. 合肥: 安徽农业大学, 2016.
[23]	YANG Y, CHEN K, CHAO L, et al. Research on hierarchy structure generation method of ontology knowledge pan-concept in agriculture[J]. Advances in Robotics & Automation, 2017, 6(3): 1-6.
[24]	余凡. 领域本体构建方法及实证研究[M]. 武汉: 武汉大学出版社, 2015: 75-79.
[25]	GUAN N, SONG D, LIAO L. Knowledge graph embedding with concepts[J]. Knowledge-Based Systems, 2019, 164(3): 38-44.
[26]	AYDIN S, AYDIN M N. Ontology-based data acquisition model development for agricultural open data platforms and implementation of OWL2MVC tool[J]. Computers and Electronics in Agriculture, 2020, 175(3): 1-9.
[27]	李涓子, 侯磊. 知识图谱研究综述[J]. 山西大学学报(自然科学版), 2017, 40(3): 454-459.
[28]	王莉军, 周越, 桂婕, 等. 基于BiLSTM-CRF的中医文言文文献分词模型研究[J]. 计算机应用研究, 2020, 37(11): 3359-3362.
[29]	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. doi: 10.6041/j.issn.1000-1298.2021.01.021
[30]	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. doi: 10.11975/j.issn.1002-6819.2020.12.021
[31]	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. doi: 10.6041/j.issn.1000-1298.2017.S0.029
[32]	KHAN N A, MOHAMMADI M. A Modified viterbi algorithm-based if estimation algorithm for adaptive directional time–frequency distributions[J]. Circuits, Systems, and Signal Processing, 2018, 38(5): 2227-2244.
[33]	PRAVEENA R K S, JUSTUS S. Concept relation knowledge visualization with CR logic using Neo4j[J]. International Journal of Recent Technology and Engineering (IJRTE), 2019, 8(4): 8475-8480. doi: 10.35940/ijrte.D9792.118419
[34]	吴运兵, 杨帆, 赖国华, 等. 知识图谱学习和推理研究进展[J]. 小型微型计算机系统, 2016, 37(9): 2007-2013. doi: 10.3969/j.issn.1000-1220.2016.09.022
[35]	于合龙, 陈程程, 林楠, 等. 互联网+农业科技服务云平台构建与农业时空推荐算法研究[J]. 吉林农业大学学报, 2019, 41(4): 495-504.
[36]	刘欣. 基于确定性因子理论的肺癌诊断Web专家系统的研究与实现[D]. 长春: 吉林大学, 2017.
[37]	靳留乾, 徐扬, 方新, 等. 基于证据理论的不确定性推理方法及其应用[J]. 计算机工程与应用, 2015, 51(10): 6-11. doi: 10.3778/j.issn.1002-8331.1410-0304

Cited By

Cited by

Periodical cited type(1)

吴迪，栗云鹏，巴洪宇，杜鹃，吴惠明，张启龙，冯小宇，周德刚. 美洲型猪繁殖与呼吸综合征病毒实时荧光RT-PCR定量检测试剂盒测量不确定度研究. 中国动物检疫. 2020(05): 94-100 .

Other cited types(2)

Get Citation

PDF

XML

Article views (2139) PDF downloads (965) Cited by(3)

Turn off MathJax

Article Contents

Abstract

References

Intelligent diagnostic system for rice diseases and pests based on knowledge graph

Abstract

References

Cited by

Periodical cited type(1)

Other cited types(2)

Catalog

Export File

Citation

Format

Content