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

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ID: 115642
2016
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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.
Reference Key
kung2016sustainabilityaccuracy Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hsu-Yang Kung;Ting-Huan Kuo;Chi-Hua Chen;Pei-Yu Tsai;Kung, Hsu-Yang;Kuo, Ting-Huan;Chen, Chi-Hua;Tsai, Pei-Yu;
Journal sustainability
Year 2016
DOI
10.3390/su8080735
URL
https://dx.doi.org/10.3390/su8080735
Keywords
Data mining stepwise regression multiple regression analysis ensemble neural network yield prediction models

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