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2020 Vol.29, Issue 4 Preview Page

Original Articles

Estimation of Sweet Pepper Crop Fresh Weight with Convolutional Neural Network

합성곱 신경망을 이용한 온실 파프리카의 작물 생체중 추정

Taewon Moon1
,
Junyoung Park1
,
Jung Eek Son23*
문 태원1
,
박 준영1
,
손 정익23*
1서울대학교 농림생물자원학부 원예생명공학전공 대학원생
2서울대학교 농림생물자원학부 원예생명공학전공 교수
3서울대학교 농업생명과학연구원 겸임연구원
1Graduate Student, Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul 08826, Korea
2Professor, Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul 08826, Korea
3Adjunct Senior Researcher, Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
*Corresponding author: sjeenv@snu.ac.kr
30 October 2020. pp. 381-387
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Abstract

Various studies have been attempted to estimate and measure the fresh weight of crops. However, no studies have used raw images of sweet peppers to estimate fresh weight. Recently, image processing research using convolution neural network (CNN) that can use raw data is increasing. In this study, the crop fresh weight was estimated by using the images of sweet peppers as inputs of CNN. The experiment was performed in a greenhouse growing sweet pepper (Capsicum annuum L.). The fresh weight, the output of the CNN, was regressed based on the data collected through destructive investigation. The highest coefficient of determination (R2) of the trained CNN was 0.95. The estimated fresh weight showed a very similar trend to the actual measured value.

Keywords
artificial neural network
deep learning
image processing
machine learning
plant growth

작물의 생체중을 추정하기 위해 다양한 연구가 시도되었지만, 이미지를 활용하여 생체중을 추정한 예는 없었다. 최근 합성곱 신경망을 사용한 이미지 처리 연구가 늘고 있으며, 합성곱 신경망은 미가공 데이터를 그대로 사용할 수 있다. 본 연구에서는 합성곱 신경망을 이용하여 미가공 데이터 상태인 특정 시점의 파프리카 이미지를 입력으로 작물의 생체중을 추정하도록 학습하였다. 실험은 파프리카(Capsicum annuum L.)를 재배하는 온실에서 수행하였다. 합성곱 신경망의 출력값인 생체중은 파괴조사를 통해 수집한 데이터를 기반으로 회귀 분석하였다. 학습된 합성곱 신경망의 결정 계수(R2)의 최고값은 0.95로 나타났다. 생체중 추정값은 실제 측정값과 매우 유사한 경향성을 보여주었다.

키워드
기계 학습
딥 러닝
식물 생장
이미지 처리
인공신경망
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Information
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Protected Horticulture and Plant Factory
  • Journal Title(Ko) :시설원예ㆍ식물공장
  • Volume : 29
  • No :4
  • Pages :381-387
  • Received Date : 2020-09-04
  • Revised Date : 2020-09-12
  • Accepted Date : 2020-09-14
  • DOI :https://doi.org/10.12791/KSBEC.2020.29.4.381
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