Browse > Article
http://dx.doi.org/10.14400/JDC.2018.16.12.293

Production of agricultural weather information by Deep Learning  

Yang, Miyeon (Dept. of Statistics, Daegu University)
Yoon, Sanghoo (Division of Mathematics and big data science, Daegu University)
Publication Information
Journal of Digital Convergence / v.16, no.12, 2018 , pp. 293-299 More about this Journal
Abstract
The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.
Keywords
Agricultural weather; Deep learning; Sliding window; Deep neural network; GloSea5;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
연도 인용수 순위
1 G. V. Oldenborgh, F. Doblas-Reyes, B. Wouters & W. Hazeleger. (2012). Skill in the trend and internal variability in a multi-model decadal prediction ensemble. Climate Dynamics, 38(7), 1263-80.   DOI
2 M. I. Jung, S. W. Son, J. Choi & H. S. Kang. (2015). Assessment of 6-Month Lead Prediction Skill of the GloSea5 Hindcast Experiment. Atmosphere, 25(2), 323-337.   DOI
3 K. H. Son, D. H. Bae & H. S. Cheong. (2015). Construction & Evaluation of GloSea5-Based Hydrological Drought Outlook System. Atmosphere, 25(2), 271-281.   DOI
4 J. S. Min, M. H. Lee, J. B. Jee & M. Jang. (2016). A Study of the Method for Estimating the Missing Data from Weather Measurement Instruments. Journal of Digital Convergence, 14(8), 245-252.   DOI
5 J. H. Ha, Y. H. Lee & Y. H. Kim. (2016). Forecasting the precipitation of the next day using deep learning. Journal of Korean Institute of Intelligent Systems, 26(2), 93-98.   DOI
6 Q. K. Tran & S. K. Song. (2017). Water Level Forecasting based on Deep Learning: A Use Case of Trinity River-Texas-The United States. Journal of KIISE, 44(6), 607-612.   DOI
7 I. H. Ryu. (2006). A Comparative Study of Time Series Forecasting By Artificial Neural Networks. Master dissertation. Yonsei University, Seoul.
8 J. S. Kim. (2013). Long-Term Runoff Prediction Using Artificial Neural Network in the Bocheong-Cheon. Master dissertation. Kyung Hee University, Seoul.
9 E. M. Yang, H. Jae. Lee & C. H. Seo. (2017). Comparison of Detection Performance of Intrusion Detection System Using Fuzzy and Artificial Neural Network. Journal of Digital Convergence, 15(6), 391-398.   DOI
10 H. S. Song. (2017). Comparison of Performance between MLP and RNN Model to Predict Purchase Timing for Repurchase Product. Journal of information technology applications & management, 24(1), 111-128.   DOI
11 K. T. Bae. (2016) Development of a Price Prediction Model of Agricultural Product using Artificial Neural Networks. Master dissertation. Soongsil University, Seoul.
12 K. K. Seo. (2015). Sales Prediction of Electronic Appliances using a Convergence Model based on Artificial Neural Network and Genetic Algorithm. Journal of Digital Convergence, 13(9), 177-182.   DOI
13 Y. Shin & S. Yoon. (2016). Electricity forecasting model using specific time zone. Journal of the Korean Data and Information Science Society, 27(2), 275-284.   DOI
14 Y. Cho & I. Kim. (2010). Predicting the Performance of Recommender Systems through Social Network Analysis and Artificial Neural Network. Journal of Intelligence and Information Systems, 16(4), 159-172.
15 M. Kim & C. Hong. (2016). The Artificial Neural Network based Electric Power Demand Forecast using a Season and Weather Informations. Journal of The Institute of Electronics and Information Engineers, 53(1), 71-78.   DOI
16 B. W. Chio. (2006). Prediction of Swell Index of Clay Using the Artificial Neural Networks. Master dissertation. Kyungpook National University, Daegu.
17 N. R. Jo. (2017). Design and Implementation of criminal Identification System Based on Deep Learning. Master dissertation, Gachon University, Gyeonggi.
18 S. Moon, S. Han, K. Choi & J. Song. (2016). Data processing system and spatial-temporal reproducibility assessment of GloSea5 model. Journal of Korea Water Resources Association, 49(9), 761-771.   DOI