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Integrating Case-Based and Rule-Based Reasoning for Diagnosis and Treatment of Mango Disease Using Data Mining Techniques

Authors

  • Wasyihun Sema Assosa University
  • Yirga Yayeh Faculty of Computing, Bahirdar Institute of Technology (BiT), Bahirdar, Ethiopia

Keywords:

Mango, Mango Pests, Knowledge-Based system, Data mining, Rule-based reasoning, Ensemble Algorithm

Abstract

Mango production in Ethiopia is widely affected by disease and attacked by several insect pests. Mango disease needs sufficient and knowledgeable agricultural experts to identify the disease and describe the methods of treatment and protection at an early stage of infestation. However, agricultural specialists’ assistance may not always be available and accessible to every farmer when the need arises for their help. Therefore, this study presents a hybrid knowledge-based system for the diagnosis and treatment of mango disease to identify the disease timely and apply the control measure effectively. The system aims to provide a guide for research centers and development agents to facilitate the diagnostic process of mango disease. To develop the proposed method, data and knowledge are acquired from documented and non-documented sources. The acquired knowledge is modeled using CommonKADS methodology which represents the concepts and procedures involved in the diagnosis of mango disease. For data mining, mango disease data are collected from the Ethiopian Agricultural Institute (EAI). The researcher uses four selected mango disease classifications (Anthracnose, Powdery Mildew, Algae Spot, and Bacterial Canker) based on the frequent spread in the study area. In terms of the techniques applied, the researcher compares the performance of ensemble algorithms with other algorithms separately. From the two experiments boosting the J48 algorithm achieve good performance with an accuracy of 86.92%. Thus, the result of boosting the J48 algorithm is combined with expert knowledge to prepare one knowledge base to build Rule-based reasoning modules. For the Case-Based reasoning module, the cases are prepared from the collected dataset using jCOLIBRI studio. Finally, the researcher uses a rule dominant approach for the integration of RBR and CBR Module. The system is developed using SWI Prolog programing language and Java Net Beans and JPL Library is used for the integration of GUI and production rules. The system has been evaluated to ensure the performance of the system is accurate and is the system usable by the researcher and development agent. The system has registered an overall performance of 90% accuracy in both system performance and user acceptance testing. Hence, this study concludes that the integration of rule-based and case-based reasoning approaches achieve better performance concerning the performance of individual reasoning approaches in the identification, recommending first-line treatment, and prevention of Mango infection. The finding of this study can be used as a supportive tool for agricultural extension workers, farmers, and farmworkers to help in the diagnosis and treatment of mango disease.

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##submission.authorBiography##

##submission.authorWithAffiliation##

Yirga Yayeh Munaye (PhD) received a B.Sc. degree in Information
Technology and M.Sc. degree in Information Science from Bahir Dar
University and Addis Ababa University, Ethiopia in 2009 and 2014,
respectively. In 2021, He got PhD Degree with Electrical Engineering and
Computer Science (EECS) from National University of Technology
(NTUT), Taiwan. From 2014-2017, he was a full-time lecturer and researcher on issues related
to Information Technology in Assosa University, Ethiopia. In addition, he was responsible for
the head department of Information Technology (2014-2016) and from the 2016-2017
coordinators of Continuous and Distance Education Program (CDEP) for the school of
Informatics in Assosa University, Assosa, Ethiopia. Currently, He is an assistant professor in
Bahir Dar Institute of Technology (BiT), Bahir Dar University, Ethiopia as a researcher and chair
holder for Networking and internet chair. His research interests are in the areas of application of
deep learning in wireless communication, ad-hoc networks, Resource management, UAV-base
station deployment, IoT, Emerging Technologies.

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