{"title":"土壤碳管理的数字孪生生物互联网(IoLT)框架","authors":"Di An, Yangquan Chen","doi":"10.1109/MESA55290.2022.10004406","DOIUrl":null,"url":null,"abstract":"Due to the lack of cost-effective methods for soil carbon content accounting, soil carbon emissions cannot be managed properly for a long time. The traditional methods are labor intensive which usually need tedious preprocessing and expensive analyzers to quantify the total soil carbon content. In this paper, we propose that a Digital Twin with AIoLT framework could perfectly solve the challenge mentioned above. By using an AIoLT-enabled proximity radar sensor to measure soil carbon content in real-time, our proposed soil carbon management digital twin (SCMDT) has the ability to deal with these soil carbon data to be “smart” big data, which are from missionaware sampling strategies. We also provide an evaluation metric for our proposed SCMDT in order to quantify its performance.","PeriodicalId":410029,"journal":{"name":"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Digital Twin Enabled Internet of Living Things (IoLT) Framework for Soil Carbon Management\",\"authors\":\"Di An, Yangquan Chen\",\"doi\":\"10.1109/MESA55290.2022.10004406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the lack of cost-effective methods for soil carbon content accounting, soil carbon emissions cannot be managed properly for a long time. The traditional methods are labor intensive which usually need tedious preprocessing and expensive analyzers to quantify the total soil carbon content. In this paper, we propose that a Digital Twin with AIoLT framework could perfectly solve the challenge mentioned above. By using an AIoLT-enabled proximity radar sensor to measure soil carbon content in real-time, our proposed soil carbon management digital twin (SCMDT) has the ability to deal with these soil carbon data to be “smart” big data, which are from missionaware sampling strategies. We also provide an evaluation metric for our proposed SCMDT in order to quantify its performance.\",\"PeriodicalId\":410029,\"journal\":{\"name\":\"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MESA55290.2022.10004406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MESA55290.2022.10004406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Digital Twin Enabled Internet of Living Things (IoLT) Framework for Soil Carbon Management
Due to the lack of cost-effective methods for soil carbon content accounting, soil carbon emissions cannot be managed properly for a long time. The traditional methods are labor intensive which usually need tedious preprocessing and expensive analyzers to quantify the total soil carbon content. In this paper, we propose that a Digital Twin with AIoLT framework could perfectly solve the challenge mentioned above. By using an AIoLT-enabled proximity radar sensor to measure soil carbon content in real-time, our proposed soil carbon management digital twin (SCMDT) has the ability to deal with these soil carbon data to be “smart” big data, which are from missionaware sampling strategies. We also provide an evaluation metric for our proposed SCMDT in order to quantify its performance.