With the recent development of agriculture, the growing area and utilization rate of facilities are increasing, but it is necessary to control and prevent pests, and if the disease is detected at an early stage, appropriate treatment is possible. To this end, researches on control systems using artificial intelligence are being expanded recently, therefore we propose a pest diagnosis system using data acquisition and deep learning through collective intelligence. This study modeled the diagnostic system based on deep learning using the collective intelligence that the user group participates in the prediction of pests arising from the plant cultivation and the data registered by experts in the field. Diagnostic data were collected information on pest diagnosis registered on the Internet and used; the collected data were constructed as a data set that is easy to analyze, through preprocessing, types of crops were classified, pests data were studied through TensorFlow. Most of the researches for the control and prevention of pests are based on web-based expert system. In this paper, we collect data through the collective intelligence and the general public. Especially, when a user uses input question and answers data without a formalized format, it gives wrong prediction; therefore, the preprocessing process was performed for data analysis because it could adversely affect the reliability of the system. After the data collection and preprocessing process is completed, a prediction model is created using TensorFlow, an artificial intelligence open source framework, using the generated data set. The user was allowed to
The purpose of this study is to introduce the establishment process, policy target, and projects for “Chungnam’s master plan on environmental health policy (2017-2020)” as the local government’s role in addressing local environmental health challenges. We first analyzed existing studies and social issues on the media related to “Chungnam’s master plan” to understand Chungnam’s environmental health status and discussed domestic and international policy trends and related plans. An environmental health perception questionnaire survey and a Delphi expert questionnaire survey were conducted among provincial residents to collect various actors’ opinions on Chungnam’s environmental health issues and policy. An expert advisory panel was launched, and a residents’ voice workshop and cities-and-guns-policy-suggestion workshop were held. The vision of Chungnam’s environmental health policy is minimizing environmental hazards. We finally selected “Pleasant environment, healthy people, happy Chungnam” to represent the will to shape a pleasant environment and prevent and manage health damages for a happy Chungnam. We selected five strategies based on status analysis and a review of domestic and international policy trends and related plans and identified 2 targets (policy objectives) to accomplish the strategies. The strategies to achieve the first target, “Leader in environmental health policy: Chungnam,” include ‘Empowering active provincial capabilities,’ ‘Setting up province-specific systems for environmental health surveys and research,’ and ‘Preventing and managing newly emerging pollutants.’ The strategies for the second target, “Everyone is healthy: Chungnam,” include ‘Relieving health inequalities among vulnerable regions and residents’ and ‘Enlarging the resident-friendly environmental health policy.’ We developed 29 projects in total, according to these strategies. The establishment of “Chungnam’s master plan” is highly valuable; we developed it through discussion involving diverse actors to address environmental health challenges together. It is necessary to continue to strengthen participation, communication, and cooperation among actors to develop an environmental health policy model for the future.
Big Data is now seen as a major asset in the company's competitiveness, its influence in the future is expected to grow. Companies that recognize the importance are already actively engaged with Big Data in product development and marketing, which are increasingly applied across sectors of society, including politics, sports. However, lack of knowledge of the system implementation and high costs are still a big obstacles to the introduction of Big Data and systems. It is an objective in this study to build a Big Data system, which is based on open source Hadoop and Hive among Big Data systems, utilizing POS sales data of small and medium-sized offline markets. This approach of convergence is expected to improve existing sales systems that have been simply focusing on profit and loss analysis. It will also be able to use it as the basis for the decisions of the executive to enable prediction of the consumption patterns of customer preference and demand in advance.
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