Jongtae Lim, Jae-soo Yoo, Christopher RETITI DIOP EMANE, Y. Kim, Jeong-Hyun Beak
{"title":"An Effective Smart Greenhouse Data Preprocessing System for Autonomous Machine Learning","authors":"Jongtae Lim, Jae-soo Yoo, Christopher RETITI DIOP EMANE, Y. Kim, Jeong-Hyun Beak","doi":"10.30693/smj.2023.12.1.47","DOIUrl":null,"url":null,"abstract":"Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data","PeriodicalId":249252,"journal":{"name":"Korean Institute of Smart Media","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Korean Institute of Smart Media","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30693/smj.2023.12.1.47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data