인공 신경망을 이용한 채소 단수 예측 모형 개발: 고추를 중심으로
Development of Yield Forecast Models for Vegetables Using Artificial Neural Networks: the Case of Chilli Pepper
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초록
This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper’s yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.
Abstract
This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper’s yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.
참고문헌 (18)
- Gunther, F., "Neuralnet: Training of Neural Networks", The R Journal, 2(1), pp.30~38 (2010)
- Guo, W. W., "Crop Yield Forecasting Using Artificial Neural Networks:A Comparison between Spatial and Temporal Models", Mathematical Problems in Engineering, 2014 (2014)
- A Study on Building Crop Yield Forecasting Model Considering Meteorological Elements, "Han, S. H.", Korea Rural Economic Institute, ~, pp.P152 (2011)
- Application of the Adaline System to Weather Forecasting, "Hu, M. J. C.", Stanford Electronic Laboratories, ~, pp.Technical Report 6775-1 (1964)
- A Demand and Supply Model of Agricultural and Livestock Products for Midterm Outlook, "Kim, B. S.", Korea Rural Economic Institute, ~, pp.M103 (2010)
- Lamba, V., "Wheat Yield Prediction Using Artificial Neural Network and Crop Prediction Techniques", International Journal for Research in Applied Science and Engineering Technology, 2(4), pp.330~341 (2014)
- Laxmi, R. R., "Weather Based Forecasting Model for Crops Yield Using Neural Network Approach", Statistics and Application, 9(1&2), pp.55~69 (2011)
- 이춘수, "생육정보를 이용한 가을배추와 가을무 단수 예측 모형 개발", 한국유기농업학회지, 25(2), pp.279~293 (2017), 한국유기농업학회
- A Study on the Production Determinants of Chinese Cabbage and Radish, "Lee, J. W.", Korea Rural Economic Institute, ~, pp.R346 (1996)
- 이민수, "머신러닝을 활용한 모돈의 생산성 예측모델", 농촌지도와 개발, 16(4), pp.939~965 (2009), 한국농촌지도학회
- A Study on Determinants of Seasonal Supply and Price of Produce in Korea: With Special Emphasis on Weather, "Lee, Y. S.", Korea Rural Economic Institute, ~, pp.R494 (2005)
- STATA Panel Data Analysis, "Min, I. S.", The Korean Association of STATA, ~ (2010)
- Panda S. S., "Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques", Remote Sensing, 2(3), pp.673~696 (2011)
- The Development of Chinese Cabbage and Radish Forecast Models, "Park, J. Y.", Korea Rural Economic Institute, ~, pp.M125 (2013)
- Rumelhart, D. E., "Learning Representations by Backprpagating Errors", Nature, 323(6188), pp.533~536 (1986)
- Rural Development Administration, "Chilli Pepper. Agricultural Technology Guide", Rural Development Administration, 126~ (2013)
- 성제훈, "인공신경망 모형을 이용하여 토양 화학성으로 벼 수확량 예측", Journal of Biosystems Engineering, 30(6), pp.360~365 (2005), 한국농업기계학회
- 윤성주, "인공 신경망을 이용한 국제곡물가격예측 모형 개발", 농업경제연구, 57(2), pp.83~101 (2016), 한국농업경제학회