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http://dx.doi.org/10.7848/ksgpc.2016.34.5.525

Convolutional Neural Networks for Rice Yield Estimation Using MODIS and Weather Data: A Case Study for South Korea  

Ma, Jong Won (Dept. of Civil and Environmental Engineering, Yonsei University)
Nguyen, Cong Hieu (Dept. of Civil and Environmental Engineering, Yonsei University)
Lee, Kyungdo (National Institute of Agricultural Science, RDA)
Heo, Joon (Dept. of Civil and Environmental Engineering, Yonsei University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.34, no.5, 2016 , pp. 525-534 More about this Journal
Abstract
In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.
Keywords
Remote Sensing; Weather Data; MODIS; Rice Yield Estimation; Convolutional Neural Network; Artificial Neural Network;
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Times Cited By KSCI : 3  (Citation Analysis)
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