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Knowledge Base System for Maize Crop Nutrients Deficiency Identification
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ahmyaw, Seid Nuru, Mohammed |
| Copyright Year | 2019 |
| Abstract | The Cereal crop maize is the common cultivated crop in Ethiopia low lands. Even though the crop is the livelihood of most of the farmers and needed to increase food production in grain, the desired agricultural productivity output in agricultural sector is very low. This is due to lack of technological and nutritional information, most of Experts cannot easily identify nutritional deficiency disorder and weak exchange of information between agricultural experts and farmers. Identifying those nutrient deficiency disorders at early stage enable to increase crop yields, fruit production and minimize frequent diseases occurrence because of nutrients deficiency. Thus, there is a need of developing knowledge base system to identify the deficiency of the maize crop and helps unexperienced field expertise in the agricultural sectors. Therefore, this study was conducted by considering the aforementioned challenges and to help mainly domain experts during identification of maize nutrient deficiency. The study has been attempted using the knowledge base system architecture and investigated different sources of knowledge to acquire from the domain through interview of experts, observation and document analysis. This source of data gathering methods were used to identify the nutrient deficiency disorder for maize. The acquired knowledge was discussed with domain experts and modeled using decision tree structure. Then the modeled knowledge is represented using rule base approach to use by prolog programming language. After this the study of the proposed model was built and the proposed model performance were tested and evaluated using system performance and user acceptance techniques. Finally, the proposed model was produced a system performance of 80% accuracy. The user acceptance testing was performed with the domain experts and the system on average scored 81.6% based on user acceptance evaluation criteria. The system has an overall accuracy of 80.8% according to the system performance and user acceptance testing. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://iosrjournals.org/iosr-jce/papers/Vol21-issue3/Series-4/C2103042126.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
