Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

Hsu-Yang Kung, Ting-Huan Kuo, Chi-Hua Chen and Pei-Yu Tsai
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Hsu-Yang Kung: Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
Ting-Huan Kuo: Department of Computer Science and Engineering, National Sun Yat Sen University, Kaohsiung 80424, Taiwan
Chi-Hua Chen: Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., Taoyuan 32661, Taiwan
Pei-Yu Tsai: Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan

Sustainability, 2016, vol. 8, issue 8, 1-11

Abstract: With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis methods are unable to handle such large amounts of data. Many studies employ neural networks, mostly specifying the number of hidden layers and neurons according to experience or formula. Different sets of network topologies have different results, and the best network model is selected. This investigation proposes a system based on the ensemble neural network (ENN). It creates multiple network models, each with different numbers of hidden layers and neurons. A model that does not achieve the accuracy rate is discarded. The proposed system derives the weighted average of all remaining network models to improve the accuracy of the prediction. This study applies the proposed method to generate agricultural yield predictions. The agricultural production process in Taiwan is more complex than those of manufacturing or other industries. The Council of Agriculture provides agricultural forecasting primarily based on the planted area and experience to predict the yield, but without consideration of the overall planting environment. This work applies the proposed data analysis method to agriculture. The method based on ENN has a much lower error rate than traditional back-propagation neural networks, while multiple regression analysis has an error rate of 12.4%. Experimental results reveal that the ENN method is better than traditional back-propagation neural networks and multiple regression analysis.

Keywords: ensemble neural network; data mining; multiple regression analysis; stepwise regression; yield prediction models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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