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2021 Vol.30, Issue 1 Preview Page

Original Articles

Identification of Sweet Pepper Greenhouse by Analysis of Environmental Data in Greenhouse

온실 내 환경데이터 분석을 통한 파프리카 온실의 식별

Na-eun Kim1
,
Kyoung-geun Lee2
,
Deog-hyun Lee1
,
Byeong-eun Moon3
,
Jae-sung Park3
,
Hyeon-tae Kim4†
김 나은1
,
이 경근2
,
이 덕현1
,
문 병은3
,
박 재성3
,
김 현태4†
1Graduate Student, Department of Bio-Systems Engineering, Graduate School of Gyeonsang National University, Jinju 52828, Korea
2Instructor, Gyeongsangnam-do Agricultural Research & Extension Services, Jinju 52733, Korea
3Research Professor, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Korea
4Professor, Department of Bio-Industrial Machinery Engineering, Gyeongsang National University(Institute of Smart Farm), Jinju 52828, Korea
1경상대학교 대학원 바이오시스템공학과 대학원생
2경상남도농업기술원 농촌지도사
3경상대학교 스마트팜연구소 학술연구교수
4경상대학교 생물산업기계공학과(스마트팜연구소) 교수
*bioani@gnu.ac.kr
31 January 2021. pp. 19-26
PDF XML Citation
Abstract

In this study, analysis was performed to identify three greenhouses located in the same area using principal component analysis (PCA) and linear discrimination analysis (LDA). The environmental data in the greenhouse were from 3 farms in the same area, and the values collected at 1 hour intervals for a total of 4 weeks from April 1 to April 28 were used. Before analyzing the data, it was pre-processed to normalize the data, and the analysis was performed by dividing it into 80% of the training data and 20% of the test data. As a result of PCA and LDA analysis, it was found that PCA classification accuracy was 57.51% and LDA classification was 67.06%, indicating that it can be classified by greenhouse. Based on the farmhouse data classified in advance, the data of the new environment can be classified into specific groups to determine the tendency of the data. Such data is judged to be a way to increase the utilization of data by facilitating identification.

Keywords
big data
classification
LDA
smart farm
PCA

본 연구에서는 같은 지역에 위치한 온실 3곳의 식별을 위해 통계적인 방법으로 분류를 하고자 주성분 분석(PCA)과 선형 판별 분석(LDA)을 수행하였다. 온실 내의 환경데이터는 같은 지역의 온실 3곳을 대상으로 4월 1일부터 4월 28일 총 4주간 1시간 간격으로 수집된 값을 사용하였다. 데이터를 분석하기 전, 데이터 정규화를 시키는 전처리를 거쳤으며, 전체의 80%인 훈련자료(training data)와 20%인 테스트 자료(test data)로 나누어 분석을 수행하였다. 분석을 수행한 결과, PC1은 57.51%의 설명력으로 PC1 = 0.7118112 × Tem. –0.6830065 × Humi. –0.1637892 × CO2.의 식을 가지며, LD1은 67.06%의 설명력으로 LD1 = 0.8622565 × Tem. –0.1805741 × Humi. + 1.4018140 × CO2. + 0.03040701의 식을 가지는 것으로 나타났다. 이렇게 미리 분류시켜 놓은 온실의 데이터를 바탕으로 새로운 환경의 데이터를 입력하였을 때 특정 그룹으로의 분류가 가능함으로써 데이터의 성향을 파악할 수 있다. 이러한 데이터는 식별을 용이하게 함으로써 데이터의 활용도를 높여주는 방법이라고 판단된다.

키워드
분류
빅데이터
스마트팜
주성분 분석
판별분석
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Information
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 30
  • No :1
  • Pages :19-26
  • Received Date : 2020-09-16
  • Revised Date : 2020-12-23
  • Accepted Date : 2020-12-24
  • DOI :https://doi.org/10.12791/KSBEC.2021.30.1.019
Journal Informaiton Journal of Bio-Environment Control Journal of Bio-Environment Control
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