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.
Highlights
Crop production is important for people in Taiwan, while manufacturing industries face more issues than agricultural production
Farmers engaged in production, or those indirectly related to agricultural agencies, need to predict their crop yield accurately to avoid imbalances in market supply and demand caused or hastened by harvest crop quality and poor results
Zhang et al (2010) [10] accumulated meteorological and crop growth data, and employed these to compare the performance of artificial neural networks, the k-nearest neighbors algorithm and regression methods to predict soybean growth and flowering stages in the schedule model
Summary
Crop production is important for people in Taiwan, while manufacturing industries face more issues than agricultural production. Issues in agricultural production include climatic factors, pests, diseases and the treatment process. Farmers engaged in production, or those indirectly related to agricultural agencies, need to predict their crop yield accurately to avoid imbalances in market supply and demand caused or hastened by harvest crop quality and poor results. To understand the effect of important meteorological parameters, and to predict crop yields effectively, this work adopts stepwise regression and an ensemble neural network (ENN) method for analysis with the aim of improving the accuracy of crop yield prediction.
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