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Developing knowledge-based system for the diagnosis and treatment of mango pests using data mining techniques

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Abstract

Detecting the pests of mango plants at an early stage requires an expert to identify the pests, describe the methods of treatment and protection. Expert systems help a great deal in identifying those diseases and pests and describing methods of treatment to be carried out. To empower the expert’s knowledge rule-based reasoning knowledge-based system is designed for the diagnosis and treatment of mango pests. In this study, the applicability of data mining techniques are demonstrated for the development of the rule-based knowledge-based system and the designed knowledge-based system helps to fill the knowledge gaps of human experts in the diagnosis and treatment of mango pests. Knowledge is acquired from a domain expert and document analysis. The acquired knowledge is modeled using hybrid knowledge modeling techniques and the modeled knowledge is represented in machine understandable format using production rules. The researcher used tools used for both knowledge modeling and knowledge representation. CommonKADS are used as knowledge modeling and prolog programming languages are used for rule representation. The knowledge extracted from boosting the J48 algorithm and the expert knowledge is integrated using integration at the decision phase approach. After the integration is done, a rule-based knowledge-based system prototype is implemented. The prototype knowledge-based system is evaluated on both system performance testing and user acceptance testing methods. Based on these evaluation techniques the overall performance of the designed model result achieves 90% accuracy. Finally, this study concludes that the integration of expert knowledge and data mining results in the development of a knowledge-based system that achieve better performance concerning the performance in the identification, recommending first-line treatment, and prevention of mango infection. The finding of this study can be used as supportive tools for agricultural extension workers, farmers, and farmworkers to help in the diagnosis and treatment of mango pests.

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References

  1. OFGAA DAKO ED (2015) Infestation of Aulacaspis tubercularis (homoptera: Diaspididae) on mango fruits at different stages of fruit development, in western Ethiopia. J Biol Agric Healthc 5(18):34–39

    Google Scholar 

  2. Ayalew G, Fekadu A, Sisay B (2016) Appearance and chemical control of white mango scale (Aulacaspis tubercularis) in Central Rift Valley. Sci Technol Arts Res J 4:59. https://doi.org/10.4314/star.v4i2.8

    Article  Google Scholar 

  3. Belay K, Abebaw D (2004) Challenges facing agricultural extension agents: a case study from south-western Ethiopia. Afr Dev Rev 16:139–168. https://doi.org/10.1111/j.1467-8268.2004.00087.x

    Article  Google Scholar 

  4. Ait Issad H, Aoudjit R, Rodrigues JJ (2019) A comprehensive review of data mining techniques in smart agriculture. Eng Agric Environ Food 12(4):511–525. https://doi.org/10.1016/j.eaef.2019.11.003. https://www.sciencedirect.com/science/article/pii/S1881836619301533

  5. Emmanuel O, Wemembu U (2019) Knowledge based management system and dearth of flexible framework for software development. West Afr J Ind Acad Res 15(1):54–60

    Google Scholar 

  6. Ahmed N, Ahammed R, Islam MM, Uddin MA, Akhter A, Talukder MA-A, Paul BK (2021) Machine learning based diabetes prediction and development of smart web application. Int J Cogn Comput Eng 2:229–241. https://doi.org/10.1016/j.ijcce.2021.12.001. https://www.sciencedirect.com/science/article/pii/S2666307421000279

  7. Sisodia D, Sisodia DS (2018) Prediction of diabetes using classification algorithms. In: Procedia computer science, vol 132, pp 1578–1585, international conference on computational intelligence and data science. https://doi.org/10.1016/j.procs.2018.05.122. https://www.sciencedirect.com/science/article/pii/S1877050918308548

  8. Rancan C, Pesado PM, Martínez RG (2007) Toward integration of knowledge based systems and knowledge discovery systems. J Comput Sci Technol 7(01):91–97

    Google Scholar 

  9. Antwi-Agyei P, Stringer LC (2021) Improving the effectiveness of agricultural extension services in supporting farmers to adapt to climate change: insights from northeastern Ghana. Clim Risk Manag 32:100304. https://doi.org/10.1016/j.crm.2021.100304. https://www.sciencedirect.com/science/article/pii/S2212096321000334

  10. Alonso F, Martínez L, Pérez A (2012) Cooperation between expert knowledge and data mining discovered knowledge: lessons learned. Expert Syst Appl 39(8):7524–7535. https://doi.org/10.1016/j.eswa.2012.01.133

    Article  Google Scholar 

  11. Eyasu K, Jimma W, Tadesse T (2020) Developing a prototype knowledge-based system for diagnosis and treatment of diabetes using data mining techniques. Ethiop J Health Sci 30:115–124. https://doi.org/10.4314/ejhs.v30i1.15

    Article  Google Scholar 

  12. Siraj M (2019) A self-learning knowledge based system for diagnosis and treatment of chronic kidney disease. Int J Educ Manag Eng 9(2):44

    Google Scholar 

  13. Fottrell E, Ahmed N, Shaha SK, Jennings H, Kuddus A, Morrison J, Akter K, Nahar B, Nahar T, Haghparast-Bidgoli H, Khan AKA, Costello A, Azad K (2018) Diabetes knowledge and care practices among adults in rural Bangladesh: a cross-sectional survey. BMJ Glob Health 3(4). https://doi.org/10.1136/bmjgh-2018-000891. arXiv:https://gh.bmj.com/content/3/4/e000891.full.pdf

  14. Saleh F, Mumu SJ, Ara F, Begum HA, Ali L (2012) Knowledge and self-care practices regarding diabetes among newly diagnosed type 2 diabetics in Bangladesh: a cross-sectional study. BMC Public Health. https://doi.org/10.1186/1471-2458-12-1112

    Article  Google Scholar 

  15. Hogeveen H, Noordhuizen-Stassen EN, Schreinemakers JF, Brand A (1991) Development of an integrated knowledge-based system for management support on dairy farms. J Dairy Sci 74(11):4377–4384. https://doi.org/10.3168/jds.S0022-0302(91)78634-7

    Article  Google Scholar 

  16. Almadhoun HR, Abu Naser SS (2018) Banana knowledge based system diagnosis and treatment. Int J Acad Pedagog Res IJAPR 2(7). http://www.ijeais.org/ijapr

  17. Prasad R, Ranjan K, Sinha A (2006) Amrapalika: an expert system for the diagnosis of pests, diseases, and disorders in Indian mango. Knowl Based Syst 19:9–21. https://doi.org/10.1016/j.knosys.2005.08.001

    Article  Google Scholar 

  18. Rodríguez-García MA, García-Sánchez F, Valencia-García R (2021) Knowledge-based system for crop pests and diseases recognition. Electronics 10(08):905. https://doi.org/10.3390/electronics10080905

    Article  Google Scholar 

  19. Bitew M, Tesema T (2019) A collaborative approach to build a KBS for crop selection: combining experts knowledge and machine learning knowledge discovery. 11:80–92. https://doi.org/10.1007/978-3-030-26630-1-8

  20. Birhanie W, Tegegne T (2019) Knowledge based system for diagnosis and treatment of mango diseases. In: International conference on information and communication technology for development for Africa. Springer, pp 11–23. https://doi.org/10.1007/978-3-030-26630-1-2

  21. Devedžić V (1999) A survey of modern knowledge modeling techniques. Expert Syst Appl 17(4):275–294. https://doi.org/10.1016/S0957-4174(99)00040-8. https://www.sciencedirect.com/science/article/pii/S0957417499000408

  22. Schreiber G, Wielinga B, Hoog R, Akkermans H, Velde W (1994) A comprehensive methodology for KBS development. IEEE Expert 9:28–37. https://doi.org/10.1109/64.363263

    Article  Google Scholar 

  23. Avdeenko TV, Makarova ES (2018) Knowledge representation model based on case-based reasoning and the domain ontology: application to the it consultation. In: IFAC-PapersOnLine, vol 51, no 11, pp 1218–1223, 16th IFAC symposium on information control problems in manufacturing INCOM 2018. https://doi.org/10.1016/j.ifacol.2018.08.424. https://www.sciencedirect.com/science/article/pii/S2405896318315519

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  1. Department of Information Technology, College of Computing, Assosa University, Assosa, Ethiopia

    Wasyihun Sema Admass

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  1. Wasyihun Sema Admass
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Admass, W.S. Developing knowledge-based system for the diagnosis and treatment of mango pests using data mining techniques. Int. j. inf. tecnol. 14, 1495–1504 (2022). https://doi.org/10.1007/s41870-022-00870-8

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  • DOI: https://doi.org/10.1007/s41870-022-00870-8

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