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Predicting Agricultural Crop Pests with Hadoop MapReduce based Decision Tree Algorithm
Abstract
Data mining is a way of exploring large pre-existing databases in order to generate new information. It is used to find a relationship between the bulky data set which is very helpful in decision making. In agriculture sector, data mining can help farmers to develop yield. Crops can be protected from vertebrate pests and diseases by predicting and enhancing crop cultivation through efficient data mining methods. The main aim of this research is classifying agricultural crop pests which are categorized by different colors. This research work includes three phases namely data preprocessing, feature selection and execution of C5.0 algorithm using map reduce. Data preprocessing has taken away the noisy data in crop pest data that offers improved accuracy. In feature selection phase, Relief filter is applied for filtering attributes of the crop pest data set instead of using full attribute set. Relief performs a selection of instances by calculating the attribute weights based upon distances. This research work proposed Map Reduce implementation of C5.0 decision tree algorithm that is giving more accurate result rapidly and holding less memory of huge crop pest data set.
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References
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