Browse > Article
http://dx.doi.org/10.7780/kjrs.2017.33.5.2.3

Rice Yield Estimation of South Korea from Year 2003-2016 Using Stacked Sparse AutoEncoder  

Ma, Jong Won (School of Civil and Environmental Engineering, Yonsei University)
Lee, Kyungdo (National Institute of Agricultural Science, RDA)
Choi, Ki-Young (Fisheries and Agriculture Department, Statistics Korea)
Heo, Joon (School of Civil and Environmental Engineering, Yonsei University)
Publication Information
Korean Journal of Remote Sensing / v.33, no.5_2, 2017 , pp. 631-640 More about this Journal
Abstract
The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.
Keywords
Deep learning; AutoEncoder; Rice yield estimation; Climatic data; Satellite data; Remote sensing;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Alvarez, R., 2009. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach, European Journal of Agronomy, 30(2): 70-77.   DOI
2 Ahn, J., J. Hur, and K. Shim, 2010. A Simulation of Agro-Climate Index over the Korean Peninsula Using Dynamical Downscaling with a Numerical Weather Prediction Model, Korean Journal of Agricultural and Forest Meteorology, 12(1): 1-10 (in Korean with English abstract).   DOI
3 Busseti, E., I. Osband, and S. Wong, 2012. Deep learning for time series modeling, Technical report, Stanford University, Stanford, CA, USA.
4 Fang, H., S. Liang, and G. Hoogenboom, 2011. Integration of MODIS LAI and vegetation index products with the CSM CERES Maize model for corn yield estimation, International Journal of Remote Sensing, 32(4): 1039-1065.   DOI
5 Hong, S. Y., J. Hur, J. B. Ahn, J. M. Lee, B. K. Min, C. K. Lee, ... and K. M. Shim, 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea, Korean Journal of Remote Sensing, 28(5): 509-520 (in Korean with English abstract).   DOI
6 Irmak, A., J. Jones, W. Batchelor, S. Irmak, K. Boote, and J. Paz, 2006. Artificial neural network model as a data analysis tool in precision farming, Transactions of the American Society of Agricultural and Biological Engineeres, 49(6): 2027-2037.
7 Liu, J. N., Y. Hu, J. J. You, and P. W. Chan, 2014. Deep neural network based feature representation for weather forecasting, Proceedings on the International Conference on Artificial Intelligence (ICAI), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), Las Vegas, NV, Jul. 21-24, p. 1.
8 Lv, Y., Y. Duan, W. Kang, Z. Li, and F. Y. Wang, 2015. Traffic flow prediction with big data : a deep learning approach, IEEE Transactions on Intelligent Transportation Systems, 16(2): 865-873.   DOI
9 Ji, B., Y. Sun, S. Yang, and J. Wan, 2007. Artificial neural networks for rice yield prediction in mountainous regions, The Journal of Agricultural Science, 145(3): 249-261.   DOI
10 Jaikla, R., S. Auephanwiriyakul, and A. Jintrawet, 2008. Rice yield prediction using a support vector regression method, Proc., Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology, 5th International Conference on, IEEE, Krabi, Thailand, May 14-17, vol. 1, pp. 29-32.
11 Kaul, M., R. L. Hill, and C. Walthall, 2005. Artificial neural networks for corn and soybean yield prediction, Agricultural Systems, 85(1): 1-18.   DOI
12 Khodayar, M. and M. Teshnehlab, 2015. Robust deep neural network for wind speed prediction, Fuzzy and Intelligent Systems (CFIS), 2015 4th Iranian Joint Congress, Zahedan, Iran, Sep. 9-11, pp. 1-5.
13 Kim, J. S., S. H. Lee, H. S. Choi, G. S. Choi, J. D. Cho, and B. N. Chung, 2008. Survey of viral diseases occurrence on major crops in 2007, Research in Plant Disease, 14(14): 1-9 (in Korean with English abstract).   DOI
14 Kim, Y., H. Lee, and S. Hong, 2013. Continuous monitoring of rice growth with a stable groundbased scatterometer system, IEEE Geoscience and Remote Sensing Letters, 10(4): 831-835.   DOI
15 KMA, 2017. Korea Meteorological Administration -Domestic climatic information, http://www.kma.go.kr/weather/climate/average_south.jsp, Accessed Jul. 1, 2017.
16 Nuarsa, I. W., F. Nishio, and C. Hongo, 2011. Rice yield estimation using Landsat ETM+ data and field observation, Journal of Agricultural Science, 4(3): 45-56.
17 Na, S.-I., J. H. Park, and J. K. Park, 2012. Development of Korean Paddy Rice yield Prediction Model (KRPM) using meteorological element and MODIS NDVI, Journal of the Korean Society of Agricultural Engineers, 54(3): 141-148 (in Korean with English abstract).   DOI
18 Na, S. I., S. Y. Hong, Y. H. Kim, K. D. Lee, and S. Y. Jang, 2013. Prediction of rice yield in Korea using paddy rice NPP index-Application of MODIS data and CASA model, Korean Journal of Remote Sensing, 29(5): 461-476 (in Korean with English abstract).   DOI
19 Ng, A., 2011. Sparse autoencoder. CS294A Lecture notes, Stanford University, Stanford, CA, USA.
20 Prasad, A. K., L. Chai, R. P. Singh, and M. Kafatos, 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters, International Journal of Applied Earth Observation and Geoinformation, 8(1): 26-33.   DOI
21 Ranzato, M, Huang, F. J., Boureau, Y. L., and Y. LeCun, 2007. Unsupervised learning of invariant feature hierarchies with applications to object recognition, Computer Vision and Pattern Recognition (CVPR), 2007 IEEE Computer Society, Minnesota, USA, Jun.18-23, pp. 1-8.
22 Yun, J. I., 2003. Predicting regional rice production in South Korea using spatial data and crop-growth modeling, Agricultural Systems, 77(1): 23-38.   DOI
23 KOSIS, 2017. Korean Statistic - Administrative District rice yield production, http://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1ET0034, Accessed Jul. 1, 2017.
24 Li, A., Liang, S., Wang, A., and J. Qin, 2007. Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques, Photogrammetric Engineering & Remote Sensing, 73(10): 1149-1157.   DOI
25 Yoo, S. H., J. Y. Choi, S. H. Lee, Y. G. Oh, and D. K. Yun, 2013. Climate change impacts on water storage requirements of an agricultural reservoir considering changes in land use and rice growing season in Korea, Agricultural Water Management, 117: 43-54.   DOI
26 Uno, Y., S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel, 2005. Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data, Computers and Electronics in Agriculture, 47(2): 149-161.   DOI
27 USDA, 2017. Crop Explorer, http://www.pecad.fas.usda.gov/cropexplorer/, Accessed Jul. 1, 2017.