{"title":"智能养虾AIoTs","authors":"Ing-Jer Huang, Shiann-Rong Kuang, Yun-Nan Chang, Chin-Chang Hung, Chang-Ru Tsai, Kai-Lin Feng","doi":"10.1109/ISOCC47750.2019.9078467","DOIUrl":null,"url":null,"abstract":"An IoT system has been built to observe and analyze shrimp and feed conditions under turbid underwater environment in typical shrimp farms. The system streams underwater videos and water quality sensor data to a cloud server where the videos are automatically enhanced and analyzed, based on AI related techniques, to identify important objects such as shrimps and feeds. To support the scalability of our system, edge devices are currently under development to perform real time video enhancement and object detection at the farm site such that only processed information are sent back to the cloud in order to reduce the burdens of the network bandwidth and the computing/storage of the cloud servers.","PeriodicalId":113802,"journal":{"name":"2019 International SoC Design Conference (ISOCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"AIoTs for Smart Shrimp Farming\",\"authors\":\"Ing-Jer Huang, Shiann-Rong Kuang, Yun-Nan Chang, Chin-Chang Hung, Chang-Ru Tsai, Kai-Lin Feng\",\"doi\":\"10.1109/ISOCC47750.2019.9078467\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An IoT system has been built to observe and analyze shrimp and feed conditions under turbid underwater environment in typical shrimp farms. The system streams underwater videos and water quality sensor data to a cloud server where the videos are automatically enhanced and analyzed, based on AI related techniques, to identify important objects such as shrimps and feeds. To support the scalability of our system, edge devices are currently under development to perform real time video enhancement and object detection at the farm site such that only processed information are sent back to the cloud in order to reduce the burdens of the network bandwidth and the computing/storage of the cloud servers.\",\"PeriodicalId\":113802,\"journal\":{\"name\":\"2019 International SoC Design Conference (ISOCC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International SoC Design Conference (ISOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISOCC47750.2019.9078467\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International SoC Design Conference (ISOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISOCC47750.2019.9078467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An IoT system has been built to observe and analyze shrimp and feed conditions under turbid underwater environment in typical shrimp farms. The system streams underwater videos and water quality sensor data to a cloud server where the videos are automatically enhanced and analyzed, based on AI related techniques, to identify important objects such as shrimps and feeds. To support the scalability of our system, edge devices are currently under development to perform real time video enhancement and object detection at the farm site such that only processed information are sent back to the cloud in order to reduce the burdens of the network bandwidth and the computing/storage of the cloud servers.
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