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유신성(Sin-Seong Yu),최기쁨(Kippeum Choi),명현(Hyun Myung),오효정(Hyo-Jung Oh) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.6
Pest managements are required preventive care before the outbreak. Recently, the correlation between pests and soil has been proved, and then it has beed increased the need for new predictive models different from existing studies. In this study, we propose prediction model of pest according to individual farms based on heterogeneous public big data. The model is focused on ‘prediction’ rather than existing ‘diagnosis’ research. In addition, we collected and refined information of various big data such as farmland, soil, pest historic records, as well as weather information, and finally utilized total of over 3.7 million data. Especially, the correlation of the collected factors was analyzed to select only the effective qualities for predicting pests. A two-step model was proposed for the prediction of pests in this study: The proposed model will be ultimately used as a preliminary study for the pest prediction and control system of ‘win-win’ using IPM(Integrated Pest Management).
최기쁨(Kippeum Choi),유신성(Sin-Seong Yu),유남희(Nam-Hee Yoo),오효정(Hyo-Jung Oh) 한국정보기술학회 2021 한국정보기술학회논문지 Vol.19 No.2
With the recent creation of a new agricultural environment and changes in weather conditions, the need to predict and prevent pests has increased and many studies are being conducted. However, previous studies do not provide information intuitively and difficult to search for information on the user’s farm due to the wide range of pest prediction. For this reason, this study proposes a web visualization method of a pest prediction and prevention model based on farm-map data, which is an electronic map of agricultural land, so that users can easily search and consume information on the farm. This study utilized a pest prediction machine-learning model based on various public bigdata. It aims to visualize the disease and pest prediction and prevention model based on IPM(Integrated Pest Management) for ecological smart farm by providing information on appropriate methods to all other adjacent farms as well as the farm of the user.