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Knowledge Based Reasoning for Agricultural Crop Management Decisions: An Experiment Using Rule Based and Artificial Neural Network Approach

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dc.contributor.advisor Ejigu, Dejene (PhD)
dc.contributor.author Kelela, Tsegaw
dc.date.accessioned 2018-12-04T09:00:10Z
dc.date.available 2018-12-04T09:00:10Z
dc.date.issued 2009-01
dc.identifier.uri http://localhost:80/xmlui/handle/123456789/14805
dc.description.abstract Studies conducted so far and annual reports frequently issued by the national bank of Ethiopia indicated that agriculture remained the main source of living for the greatest portion of Ethiopia’s population. Despite the fact that government is giving attention to the sector, most farmers are still using their traditional knowledge to solve crop related problems. The very limited number of specialized experts in the area, coupled with lack of appropriate technologies, contributed to the present low productive land and low income status of Ethiopian farmers. Maximizing yield potential and quality need the presence of proper expert decisions at a field level. In situations where there is a shortage of high level domain experts, automating crop management decision making has paramount importance. Expert system technologies can be used for automating crop management decisions as they have been effectively applied to solve problems in other domain areas of similar nature. Based on the information gathered at the start of this research, the problem of the country’s agricultural human resource is more intense in the area of vegetable production. With this background, this research is conducted to develop an expert system model as an attempt to automate the reasoning strategy of human vegetable experts. There are a number of approaches to develop expert systems ranging from rule based methods that represent knowledge in the form of IF-THEN rules to systems that employ machine learning techniques. The approach adopted in this research uses the combination of the rule based and neural network methods with an aim to exploit the best features of the two methods. The system is modeled to have hybrid architecture by integrating rule based and neural network modules as a component of one single system. In the course of building the hybrid model, knowledge acquisition, data preprocessing, rule generation, knowledge representation and model integration tasks had been performed. In the rule based module of the hybrid model, knowledge of vegetable experts was represented as rules. To build the neural network module and perform the integration with the rule basedmodule, the fast artificial neural network libraries written in the C language were used after compiling and importing them in the prolog environment. The neural network module is built to handle user requests that may go beyond the capability of the rule based module. The artificial neural network module was integrated with the rule based module to create the hybrid vegetable expert system. To measure the effect on performance after integration, ten random queries of consultation requests were presented to both the rule based module and the hybrid system. The hybrid system responded to eight of them while the rule based module alone provided answers to only five of these questions. The performance gain observed in the hybrid system is due to the neural network module embedded in it. The result obtained in this work showed that integration of the two approaches into one system produced better result and it is encouraging to advance the system into fully functional vegetable expert system. en_US
dc.language.iso en en_US
dc.publisher Addis Ababa University en_US
dc.subject Agricultural Crop Management Decisions: en_US
dc.title Knowledge Based Reasoning for Agricultural Crop Management Decisions: An Experiment Using Rule Based and Artificial Neural Network Approach en_US
dc.type Thesis en_US


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