Browse > Article
http://dx.doi.org/10.5389/KSAE.2021.63.6.077

Estimation of Duck House Litter Evaporation Rate Using Machine Learning  

Kim, Dain (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Lee, In-bok (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Yeo, Uk-hyeon (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Lee, Sang-yeon (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Park, Sejun (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Decano, Cristina (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Kim, Jun-gyu (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Choi, Young-bae (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Cho, Jeong-hwa (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Jeong, Hyo-hyeog (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Kang, Solmoe (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University)
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.63, no.6, 2021 , pp. 77-88 More about this Journal
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;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Shin, H. Y., H. K. Yim, and W. T. Kim, 2018. Intelligent green house control system based on deep learning for saving electric power consumption. Institute of Korean Electrical and Electronics Engineers: 53-60 (in Korean). doi:10.7471/ikeee.2018.22.1.53.   DOI
2 Shakoor, M. T, K. Rahman, S. N. Rayta, and A. Chakrabarty, 2017. Agricultural production output prediction using supervised machine learning techniques. 2017 1st International Conference on Next Generation Computing Applications (NextComp):182-187. doi:10.1109/NEXTCOMP.2017.8016196.   DOI
3 Cho, Y. H., Y. D. Seo, D. J. Park, and J. C. Jeong, 2016. Study on the activation functions for efficient learning in DNN. The Institute of Electronics and Information Engineers: 800-803 (in Korean).
4 Bernhart, M., and O. O. Fasina, 2009. Moisture effect on the storage, handling and flow properties of poultry litter. Waste Management 29: 1392-1398. doi:10.1016/j.wasman.2008.09.005.   DOI
5 Wang, H. K., L. Li, W. Yong, M. Fanjia, H. H. Wang, and N. A. Sigrimis, 2018. Recurrent Neural Network model for prediction of microclimate in solar greenhouse. IFAC PapersOnLine 51(17): 790-795. doi : 10.1016/j.ifacol.2018.08.099.   DOI
6 Korean Duck Association, 2019. A report of research on the improvement of duck breeding facilities I. Duck Village 195: 24-35 (in Korean).
7 Korean Duck Association, 2019. A report of research on the improvement of duck breeding facilities II. Duck Village 196: 20-40 (in Korean).
8 Bang, H. T., D. W. Kim, H. B. Jong, J. C. Na, H. K. Kang, M. J. Kim, M. M. H. Mushtaq, H. C. Choi, S. B. Lee, M. Kang, and J. H. Kim, 2013. Effect of various forms of floor system on performance of meat-type duck and environments of duck house. Korean Journal Society of Poultry Science 40(3): 253-262 (in Korean). doi:10.5536/ KJPS.2013.40.3.253.   DOI
9 Alhnaity, B., S. Pearson, G. Leontidis, and S. Kollias, 2020. Using deep learning to predict plant growth and yield in greenhouse environments. Acta Hortic 1296: 425-432. doi:10.17660/ActaHortic.2020.1296.55.   DOI
10 Efron B., and R. J. Tibshirani, 1998. An Introduction to the bootstrap. Monographs on statistics and applied probability 57. London: Chapman & Hall/CHC.
11 Bang, H. T., H. C. Choi, H. S. Chae, J. C. Na, H. G. Kang, M. J. Kim, D. W. Kim, S. B. Park, S. H. Jung, and O. S. Seo, 2010. A study on the productivity and environmental change according to duck house litter. Korean Society of Poultry Science 27: 132-134 (in Korean).
12 Korean Duck Association, 2019. A report of research on the improvement of duck breeding facilities III Duck Village 197: 30-51 (in Korean).
13 Statistics Korea, 2020. Results of the farm and fishery household economy survey in 2020.
14 Statistics Korea, 2020. 2020 Livestock production cost survey.
15 Kwon, K. S., J. S. Woo, J. H. Noh, S. I. Oh, J. B. Kim, J. K. Kim, K. Y. Yang, D. H. Jang, and S. M. Choi, 2021. Development and field-evaluation of automatic spreader for bedding materials in duck houses. Journal of the Korean Society of Agricultural Engineers 63(1): 37-48 (in Korean). doi:10.5389/KSAE.2021.63.1.037.
16 Akinoglu, B. G., 1991. A review of sunshine-based models used to estimate monthly average global solar radiation. Renewable Energy 1(3): 479-497.   DOI
17 Bakay, M. S., and U. Agbulut, 2021. Electricity production based forecasting of greenhouse gas emissions in turkey with deep learning, Support Vector Machine and Artificial Neural Network algorithms. Journal of Cleaner Production 285: 1-18.
18 Kim, Y. H., 2017. Analysis of ventilation efficiency of standard duck house using computational fluid dynamics. Seoul National University: 1-91 (in Korean).
19 Hong, E. C., B. S. Kang, W. K. Kang, J. J. Jeon, H. S. Kim, J. S. Son, and C. H. Kim, 2019. Effect of different stocking densities in plastic wired-floor house on performance and uniformity of Korean native commercial ducks. Korean Journal Society of Poultry Science 46(4): 215-221 (in Korean). doi:10.5336/KJPS.2019.46.4.215.   DOI
20 Lee, S. Y., I. B. Lee, R. W. Kim, U. H. Yeo, J. G. Kim, and K. S. Kwon, 2020. Dynamic energy modeling for analysis of the thermal and hygroscopic environment in a mechanically ventilated duck house, Biosystems Engineering, 200: 431-449. doi:10.1016/j.biosystemseng.2020.10.015   DOI
21 ASTM, Standard test methods for laboratory determination of water (moisture) content of soil and rock by mass (Designation: D2216-10).
22 Kim, S. H., S. Y. Park, S. J. Lee, and K. S. Ryu, 2003. Effect of feeding lactobacillus reuteri to broiler on growing performance, intestinal microflora and environmental factor. Korean Society of Poultry Science 30(1): 17-28 (in Korean).
23 Lee, W. S., S. M. Park, T. W. Ban, S. H. Kim, J. Y. Ryu, and K. Y. Sung, 2018. Health monitoring of livestock using neck sensor based on machine learning. Journal of the Korea Institute of Information and Communication Engineering 22(11): 1421-1427 (in Korean). doi:10.6109/jkiice.2018. 22.11.1421.
24 Gorczyca, M. T., H. F. M. Milan, A. S. C. Maia, and K. G. Gerbremedhin, 2018. Machine learning algorithms to predict core, skin and hair-coat temperatures of piglets. Computers and Electronics in Agriculture 151: 286-294. doi:10.1016/j.compag.2018.06.028.   DOI
25 Dunlop, M. W., P. J. Blackall, and R. M. Stuetz, 2015. Water addition, evaporation and water holding capacity of poultry litter. Science of the Total Environment 538: 979-985. doi:10.1016/j.scitotenv.2015.08.092.   DOI
26 Yeo, U. H., Y. S. Cho, K. S. Kwon, T. H. Ha, S. J. Park, R. W. Kim, S. Y. Lee, S. N. Lee, I. B. Lee, and I. H. Seo, 2015. Analysis of ventilation efficiency for the summer about a design plan of standard duck house using CFD. Korean Journal Society of Poultry Science 57(5): 51-60 (in Korean). doi:10.5389/KSAE.2015.57.5.051.   DOI
27 Lee, S. Y., I. B. Lee, R. W. Kim, U. H. Yeo, C. Decano, J. G, Kim, Y. B. Choi, Y. M. Park, and H. H. Jeong, 2019. Assessment of evaporation rates from litter of duck house, Journal of the Korean Society of Agricultural Engineers 61(5): 101-108 (in Korean). doi:10.5389/KSAE.2019.61.5.101.
28 Brye, K. R., N. A. Slaton, R. J. Norman, and M. C. Savin, 2005. Short-term effects of poultry litter form and rate on soil bulk density and water content. Communications in Soil Science and Plant Analysis 35(15 & 16): 2311-2325. doi:10.1081/LCSS-200030655.   DOI
29 Dunlop, M. W., J. McAuley, P. J. Blackall, and R. M. Stuetz, 2016. Water activity of poultry litter: relationship to moisture content during a grow-out. Journal of Environmental Management 172: 201-206. doi:10.1016/j.jenvman.2016.02.036.   DOI
30 Lee, W. S., J. Y. Ryu, T. W. Ban, S. H. Kim, and H. C. Choi, 2017. Prediction of water usage in pig farm based on machine learning. Journal of the Korea Institute of Information and Communication Engineering 21(8): 1560-1566 (in Korean). doi:10.6109/jkiice.2017.21.8.1560.   DOI
31 Wadud, S., A. Michaelsen, E. Gallagher, G. Parcsi, O. Zemb, R. Stuetz, and M. Manefield, 2012. Bacterial and fungal community composition over time in chicken litter with high or low moisture content. British Poultry Science 53(5): 561-569. doi:10.1080/00071668.2012.723802.   DOI
32 Lee, W. S., J. Y. Ryu, T. W. Ban, S. H. Kim, S. K. Kang, Y. H. Ham, and H. J. Lee, 2018. Estimation of body core temperature of cow using neck sensor based on machine learning. Journal of the Korea Institute of Information and Communication Engineering 22(12): 1611-1617 (in Korean). doi:106109/ jkiice.2018.22.12.1611.
33 Miles, D. M., D. E. Rowe, and T. C. Cathcart, 2011. Litter ammonia generation: moisture content and organic versus inorganic bedding materials. Poultry Science 90: 1162-1169. doi:10.3382/ps.2010-01113.   DOI
34 Lee, W. S., K. Y. Sung, T. W. Ban, and Y. H. Ham, 2020. Production performance prediction of pig farming using machine learning. Journal of the Korea Institute of Information and Communication Engineering 24(1): 130-133 (in Korean). doi:10.6109/jkiice.2020.24.1.130.   DOI
35 Gorczyca, M. T., 2019. Machine learning applications for monitoring heat stress in livestock. Faculty of the Graduate School of Cornell University: 1-64.
36 Wang, Y., X. Yong, Z. F. Chen, H. Y. Zheng, J. Y. Zhuang, and J. J. Liu, 2018. The design of an intelligent livestock production monitoring and management system. 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS): 1-5 doi:10.1109/DDCLS.2018.8516021.   DOI
37 Hong, S. E., T. J. Park, J. I. Bang, and H. J. Kim, 2020. A study on the prediction model for tomato production and growth using ConvLSTM. The Journal of Korean Institute of Information Technology 18(1): 1-10 (in Korean). doi:10.14801/jkiit.2020.18.1.1.   DOI
38 Bakirci, K. 2009. Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey. Energy 34: 485-501. doi:10.1016/j.energy.2009.02.005.   DOI
39 Choi, I. H., and Nahm K. H., 2004. Effects of applying two different chemical additives to the litter on broiler performance and the carbon dioxide gas production in poultry houses. Korean Society of Poultry Science 31(3): 171-176 (in Korean).
40 Gouda, S. G., Z. Hussein, S. Luo, and Q. X. Yuan, 2019. Model selection for accurate daily global solar radiation prediction in China. Journal of Cleaner Production 221: 132-144. doi:10.1016/j.jclepro.2019.02.211.   DOI