Comparison and Retrieval of Situations in the Case-Based Reasoning System for Smart-Farm
Keywords:
case-based reasoning, decision making, neural network, neural network architecture, smart farmAbstract
The trend of development of smart farms is aimed at their becoming fully autonomous, robotic enterprises. The prospects for the intellectualization of agricultural production and smart farms, in particular, today are associated with the development of technology systems used to detect, recognize complex production situations and search for effective solutions in these situations. The article presents the concept of such a decision support system on smart farms using the method of decision support based on case-based reasoning - CBR system. Its implementation requires a number of non-trivial tasks, which include, first of all, the tasks of formalizing the presentation of situations and creating methods for comparing and retrieving situations from the KB on this basis. In this study, a smart farm is presented as a complex technological object consisting of interrelated components, which are the technological subsystems of a smart farm, the products produced, the objects of the operational environment, as well as the relationships between them. To implement algorithms for situational decision-making based on precedents, a formalized representation of the situation in the form of a multivector is proposed. This allowed us to develop a number of models of the trained similarity function between situations. The conducted experiments have shown the operability of the proposed models, on the basis of which ensemble architecture of a neural network has been developed for comparing situations and selecting them from the knowledge base in decision-making processes. Of practical interest is monitoring the condition of plants by their video and photo images, which allows detecting undesirable plant conditions (diseases), which can serve as a signal to activate the process of searching for solutions in the knowledge base.
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4. Chiu M.-C., Yan W.-M., Bhat S.A., Huang N.-F. Development of smart aquaculture farm management system using IoT and AI-based surrogate models. Journal of Agriculture and Food Research. 2022. vol. 9. no. 100357. DOI: 10.1016/j.jafr.2022.100357.
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11. Cho S., Kim T., Jung D.-H., Park S.H., Na Y., Ihn Y.S., Kim K.G. Plant growth information measurement based on object detection and image fusion using a smart farm robot. Computers and Electronics in Agriculture. 2023. vol. 207. no. 107703. DOI: 10.1016/j.compag.2023.107703.
12. Cerutti J., Abi-Zeid I., Lamontagne L., Lavoie R., Rodriguez-Pinzon M.J. A case-based reasoning tool to recommend drinking water source protection actions. Journal of Environmental Management. 2023. vol. 331. no. 117228. DOI: 10.1016/j.jenvman.2023.117228.
13. Zhai Z., Martínez J.F., Martínez N.L., Díaz V.H. Applying case-based reasoning and a learning-based adaptation strategy to irrigation scheduling in grape farming. Computers and Electronics in Agriculture. 2020. vol. 178. no. 105741. DOI: 10.1016/j.compag.2020.105741.
14. Wang D., Wan K., Ma W. Emergency decision-making model of environmental emergencies based on case-based reasoning method. Journal of Environmental Management. 2020. vol. 262(9). 110382. DOI: 10.1016/j.jenvman.2020.110382.
15. Mathisen B.M., Bach K., Aamodt A. Using extended siamese networks to provide decision support in aquaculture operations. Applied Intelligence. 2021. vol. 51(1). DOI: 10.1007/s10489-021-02251-3.
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17. Skobelev P.O., Simonova E.V., Budaev D.V., Voshhuk G.Ju., Larjuhin V.B. [Cloud intelligent system SMART FARMING for precision farming management]. Materialy konferencii «Informacionnye tehnologii v upravlenii (ITU-2018)» [Materials of the conference "Information Technologies in Management (ITU-2018)"]. St. Petersburg, Concern "Concern" Central Research Institute "Elektropribor", 2018. pp. 261–270.
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19. Guo Y., Zhang B., Sun Y., Jiang K., Wu K. Machine learning based feature selection and knowledge reasoning for CBR system under big data. Pattern Recognition. 2021. vol. 112(6). no. 107805. DOI: 10.1016/j.patcog.2020.107805.
20. Smiti A., Elouedi Z. Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems. Theoretical Computer Science. 2020. vol. 817. pp 24–32. DOI: 10.1016/j.tcs.2019.06.026.
21. Liao T.W., Zhang Z., Mount C.R. Similarity measures for retrieval in case-based reasoning systems. Applied Artificial Intelligence. 1998. vol. 12(4). pp. 267–288. DOI: 10.1080/088395198117730.
22. Fan Z.-P., Li Y.-H., Wang X., Liu Y. Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosion. Expert Systems with Applications. 2014. vol. 41(5). pp. 2526–2534. DOI: 10.1016/j.eswa.2013.09.051.
23. Oyelade O.N., Ezugwu A.E. A case-based reasoning framework for early detection and diagnosis of novel coronavirus. Informatics in Medicine Unlocked. 2020. vol. 20(6). no. 100395. DOI: 10.1016/j.imu.2020.100395.
24. Gabel T., Godehardt E. Top-down induction of similarity measures using similarity clouds. International Conference on Case-Based Reasoning. 2015. pp. 149–16. DOI: 10.1007/978-3-319-24586-7_11.
25. Mathisen B.M., Aamodt A., Bach K., Langseth H. Learning similarity measures from data. Progress in Artificial Intelligence. 2020. vol. 9. pp. 129–143. DOI: 10.1007/s13748-019-00201-2.
26. Glukhikh I., Glukhikh D. Case-Based Reasoning with an Artificial Neural Network for Decision Support in Situations at Complex Technological Objects of Urban Infrastructure. Applied System Innovation. 2021. vol. 4(73). 12 p. DOI: 10.3390/asi4040073.26.
27. Gluhih I.N., Gluhih D.I. [Algorithms for generating training sets in a system with case-based inference based on example situations]. Programmnye produkty i sistemy – Software&Systems. 2022. vol. 35. no. 4. pp. 660–669. (In Russ.).
28. Myttenaere A.D., Golden B., Grand B.L., Rossi F. Mean Absolute Percentage Error for regression models. Neurocomputing. 2016. vol. 192. pp. 38–48. DOI: 10.1016/j.neucom.2015.12.114.
29. Wang Y., Wang L., Li Y., He D., Liu T.-Y., Chen W. A Theoretical Analysis of NDCG Type Ranking Measures. Computer Science. 2013. 26 p. DOI: 10.48550/arXiv.1304.6480.
30. Taylor J.R. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. Second Edition. Paperback & Clothbound, 1997. 327 p.
31. Paulson P., Juell P. Using Reinforcement Learning for Similarity Assessment in Case-Based Systems. IEEE Intelligent Systems. 2003. vol. 18. no. 4. pp. 60–67. DOI: 10.1109/MIS.2003.1217629.
32. Glukhikh I., Chernysheva T., Glukhikh D. Neural Network Models for Situation Similarity Assessment in hybrid-CBR. Journal of Intelligent & Fuzzy Systems. 2023. vol. 44(15). pp. 1–14. DOI: 10.3233/JIFS-221335.
Published
2023-07-06
How to Cite
Glukhikh, I., Prokhoshin, A., & Glukhikh, D. (2023). Comparison and Retrieval of Situations in the Case-Based Reasoning System for Smart-Farm. Informatics and Automation, 22(4), 853-879. https://doi.org/10.15622/ia.22.4.6
Section
Digital Information Telecommunication Technologies
Copyright (c) Игорь Николаевич Глухих, Алексей Сергеевич Прохошин, Дмитрий Игоревич Глухих
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