USE OF DATA MINING TECHNIQUES IN AGRICULTURE
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Abstract
In modern era, agriculture sector has many challenges facing by farming communities. Numbers of services with ICT tools are being provided by government/ private leading departments to support agriculture activities and increase productivity but still needs to indentify information gaps that may help in effective decision making. There are need to address the efficient use of data mining techniques so that effective and accurate decision making can be done. This study presents a review of literature done by different authors. Experiments related to supervised learning (classification) techniques has also been done. Content which has been covered in literature review are application of data mining, machine learning, artificial intelligence in different sub fields of agriculture like disease detection, yield prediction, crop quality etc. Classification algorithms have been used to classify different types of crops. Five classification algorithms NB, SVM, NN and Decision Tree have been selected. Comparison of different parameters such as Accuracy, Kappa Statistic, RMSE and so on related to classification model have been done. It has been found that the decision tree (PART) algorithm is more suitable than other classifier on selected dataset.
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