(2021) 기계학습을 활용한 오리사 바닥재 수분 발생량 분석
Estimation of Duck House Litter Evaporation Rate Using Machine Learning
학회지 : Journal of the Korean Society of Agricultural Engineers, Vol. 63, No. 6, pp.77~88
저자 : 김다인, 이인복, 여욱현, 이상연, 박세준, 크리스티나, 김준규, 최영배, 조정화, 정효혁, 강솔뫼
개요
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
Download Link :
DOI : https://www.koreascience.or.kr/article/JAKO202134255834639.page
Estimation of Duck House Litter Evaporation Rate Using Machine Learning
학회지 : Journal of the Korean Society of Agricultural Engineers, Vol. 63, No. 6, pp.77~88
저자 : 김다인, 이인복, 여욱현, 이상연, 박세준, 크리스티나, 김준규, 최영배, 조정화, 정효혁, 강솔뫼
개요
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
Download Link :
DOI : https://www.koreascience.or.kr/article/JAKO202134255834639.page