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Sustainability 2016, 8(8), 735; doi:10.3390/su8080735

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

Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
Department of Computer Science and Engineering, National Sun Yat Sen University, Kaohsiung 80424, Taiwan
Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., Taoyuan 32661, Taiwan
Department of Information Management and Finance, National Chiao Tung University, Hsinchu 30010, Taiwan
Author to whom correspondence should be addressed.
Academic Editor: Douglas H. Constance
Received: 13 June 2016 / Revised: 18 July 2016 / Accepted: 28 July 2016 / Published: 1 August 2016
View Full-Text   |   Download PDF [2813 KB, uploaded 1 August 2016]   |  


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. View Full-Text
Keywords: ensemble neural network; data mining; multiple regression analysis; stepwise regression; yield prediction models ensemble neural network; data mining; multiple regression analysis; stepwise regression; yield prediction models

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kung, H.-Y.; Kuo, T.-H.; Chen, C.-H.; Tsai, P.-Y. Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method. Sustainability 2016, 8, 735.

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