A collaborative approach to build a KBS for crop selection: combining experts knowledge and machine learning knowledge discovery

Selecting proper crops for farmland involves a sequence of activities. These activities and the entire process of farming require a help of expert knowledge. However, there is a shortage of skilled experts who provide advice for farmers at district level in developing countries. 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.