• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.203-213
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• 2014

In this study, we proposed a new model with ISOIA (Insect Search by Observation based on Insect Appearance) method based on observation by insect appearance to improve user satisfaction, and compared it with the ISBC and ISOBC methods. In order to test these three insect search systems with AHP method, we derived three evaluation criteria for user satisfaction and three sub-evaluation criteria by evaluation criterion. In the ecological environment, non-experts need insect search systems to identify insect species and to get u-Learning contents related to the insects. To assist the public the non-experts, ISBC (Insect Search by Biological Classification) method based on biological classification to search insects and ISOBC (Insect Search by Observation based on Biological Classification) method based on the inference that identifies the observed insect through observation according to biological classification have been provided. In the test results, we found the order of priorities was ISOIA, ISOBC, and ISBC. It shows that the ISOIA system proposed in this study is superior in usage and quality compared with the previous insect search systems.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.175-187
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• 2011

Since insects play important roles in existence of plants and other animals in the natural environment, they are considered as necessary biological resources from the perspectives of those biodiversity conservation and national utilization strategy. For the conservation and utilization of insect species, an observational learning environment is needed for non-experts such as citizens and students to take interest in insects in the natural ecosystem. The insect identification is a main factor for the observational learning. A current time-consuming search method by insect classification is inefficient because it needs much time for the non-experts who lack insect knowledge to identify insect species. To solve this problem, we proposed an smart phone-based insect identification inference system that helps the non-experts identify insect species from observational characteristics in the natural environment. This system is based on the similarity between the observational information by an observer and the biological insect characteristics. For this system, we classified the observational characteristics of insects into 27 elements according to order, family, and species, and proposed similarity indexes to search similar insects. In addition, we developed an insect identification inference prototype system to show this study's viability and performed comparison experimentation between our system and a general insect classification search method. As the results, we showed that our system is more effective in identifying insect species and it can be more efficient in search time.