Abstract
This study proposed designing knowledge based solution through the collaboration of experts’ knowledge with the machine learning knowledge base to recommending suitable agricultural crops for a farm land. To design the collaborative approach the knowledge was acquired from document analysis, domain experts’ interview and hidden knowledge were extracted from Ethiopia national meteorology agency weather dataset and from central statistics agency crop production dataset by using machine learning algorithms. The study follows the design science research methodology, with CommonKADS and HYBRID models; and WEKA, SWI-Prolog 7.32 and Java NetBeans tools for the whole process of extracting knowledge, develop the knowledge base and for developing graphical user interface respectively.
Based on the objective measurement PART rule induction have the highest classifier algorithm which classified correctly 82.6087% among 9867 instances. The designed collaborative approach of experts’ knowledge with the knowledge discovery for agricultural crop selections based on the domain expert, farmers and agriculture extension evaluation 95.23%, 82.2% and 88.5% overall performance respectively.
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Acknowledgment
We would like thank to all Gondar agricultural research center cereal crop research teams for their expert knowledge and guidance in developing the domain expert knowledge base, farmers and agricultural extensions for willing to evaluate the designed collaborative approach and developed prototype. We thank to the central statics agency and Ethiopian national meteorology agency staffs for providing the necessary data and their support regarding to the nature of the data to select relevant features used for conducting this research.
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Anley, M.B., Tesema, T.B. (2019). A Collaborative Approach to Build a KBS for Crop Selection: Combining Experts Knowledge and Machine Learning Knowledge Discovery. In: Mekuria, F., Nigussie, E., Tegegne, T. (eds) Information and Communication Technology for Development for Africa. ICT4DA 2019. Communications in Computer and Information Science, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-26630-1_8
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