Designing A Case Based Reasoning System For Identifying Suitable Land For Cereal Crops

Abstract

Agriculture is the basis of the Ethiopian economy. The country’s economic development depends, in large part on sustainable improvements in agriculture However; its productivity is not kept pace with population growth. In Ethiopia, there is scarcity of agriculture experts; the available once may not be accessible to every farmer. By having an agricultural knowledge based system, the problem of experts in Agriculture can be reduced. It is therefore the aim of this study to develop a Case-based system that enable to make proper decision in the process of land and cereal crop matching so as to select suitable cereal crops for the farm unit under cultivation. This research was conducted following design science approach. Purposive sampling technique was used to select 10 domain experts for knowledge acquisition. To develop land cereal crop matching case based reasoning system, important knowledge was acquired through interview and document analysis. The acquired knowledge was modeled using hierarchical decision tree. jCOLIBRI and ArcGIS was used for developing case-based system (CBS). The developed CBS provide a method in the process of land cereal crop matching proposing a solution to a new problem or providing relevant experiences to the decision maker. GIS tools were used for preparing, handling and generating spatial and non-spatial information as a tabular form for CBR tools. The prototype of CBRLCCM system utilizes multiple knowledge to determine suitable, optimal cereal crops for a farm unit. This knowledge consists of representative cases to reflect physical, economic, environmental and social factors that affect the choice of land use for cereal crops. Domain experts’ evaluation by visual interaction with the prototype achieves 84% user acceptance. In addition, performance of the prototype system was evaluated using case testing method which scores f-Measure of 76%. This system is promising to develop an applicable system for improving the productivity of farmers by assisting agricultural expert and development agents who advise farmers on their daily needs. However, further study should be done to include inputs from climate prediction model so as to predict future land use choice.