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2021 Vol.63, Issue 6 Preview Page
Estimation of Duck House Litter Evaporation Rate Using Machine Learning

기계학습을 활용한 오리사 바닥재 수분 발생량 분석

Dain Kima
,
In-bok Leeb†
,
Uk-hyeon Yeoc
,
Sang-yeon Leec
,
Sejun Parkd
,
Decano Cristinae
,
Jun-gyu Kimd
,
Young-bae Choie
,
Jong-hwa Choe
,
Hyo-hyeog Jeongf
,
Solmoe Kangf
김 다인a
,
이 인복b†
,
여 욱현c
,
이 상연c
,
박 세준d
,
크리스티나 e
,
김 준규d
,
최 영배e
,
조 정화e
,
정 효혁f
,
강 솔뫼f
aUndergraduate Student, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University
bFull professor, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University
cResearch assistant professor, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University
dResearcher assistant professor, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University
ePh.D. Student, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University
fM.S. Student, Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University
a서울대학교 농생명과학대학 농업생명과학부 학부생
b서울대학교 농생명과학대학 농업생명과학연구소 전임교수
c서울대학교 농생명과학대학 농업생명과학연구소 조교수
d서울대학교 농생명과학대학 농업생명과학연구소 조교수
e서울대 농생명과학대학 농생명과학부 박사학생
f서울대학교 농업생명과학대학 농업생명과학부 석사학생
† Corresponding author Tel.: +82-2-880-4586, Fax: +82-2-873-2087 E-mail: iblee@snu.ac.kr
2021. pp. 77-88
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Abstract

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

Keywords
Duck house
litter
machine learning
regression model
water generation
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Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 63
  • No :6
  • Pages :77-88
  • Received Date : 2021-10-07
  • Revised Date : 2021-10-29
  • Accepted Date : 2021-11-08
  • DOI :https://doi.org/10.5389/KSAE.2021.63.6.077
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