B. Neethu, S. Jayanthy, J JudesonAntonyKovilpillai.
{"title":"改进K均值聚类算法的温室监测与控制","authors":"B. Neethu, S. Jayanthy, J JudesonAntonyKovilpillai.","doi":"10.1109/I-SMAC47947.2019.9032656","DOIUrl":null,"url":null,"abstract":"An embedded system is developed for monitoring and controlling the parameters that affect the growth of plants using STM32F401RE ARM Cortex M4 based Microcontrollers. Parameters such as Light intensity, Soil Moisture, CO2, Temperature, are monitored. The measured values are processed using Modified K Means Clustering Algorithm to find if the values are needed to be optimized to the required level to enhance the plant growth. The results are compared with the Traditional K-Means Clustering algorithm. The results indicate that the proposed algorithm gives better results in terms of accuracy and execution time compared to traditional one. The data that are measured and predicted are viewed using Cool Term.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Greenhouse Monitoring and Controlling using Modified K Means Clustering Algorithm\",\"authors\":\"B. Neethu, S. Jayanthy, J JudesonAntonyKovilpillai.\",\"doi\":\"10.1109/I-SMAC47947.2019.9032656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An embedded system is developed for monitoring and controlling the parameters that affect the growth of plants using STM32F401RE ARM Cortex M4 based Microcontrollers. Parameters such as Light intensity, Soil Moisture, CO2, Temperature, are monitored. The measured values are processed using Modified K Means Clustering Algorithm to find if the values are needed to be optimized to the required level to enhance the plant growth. The results are compared with the Traditional K-Means Clustering algorithm. The results indicate that the proposed algorithm gives better results in terms of accuracy and execution time compared to traditional one. The data that are measured and predicted are viewed using Cool Term.\",\"PeriodicalId\":275791,\"journal\":{\"name\":\"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I-SMAC47947.2019.9032656\",\"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 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SMAC47947.2019.9032656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Greenhouse Monitoring and Controlling using Modified K Means Clustering Algorithm
An embedded system is developed for monitoring and controlling the parameters that affect the growth of plants using STM32F401RE ARM Cortex M4 based Microcontrollers. Parameters such as Light intensity, Soil Moisture, CO2, Temperature, are monitored. The measured values are processed using Modified K Means Clustering Algorithm to find if the values are needed to be optimized to the required level to enhance the plant growth. The results are compared with the Traditional K-Means Clustering algorithm. The results indicate that the proposed algorithm gives better results in terms of accuracy and execution time compared to traditional one. The data that are measured and predicted are viewed using Cool Term.