Survey on Classification Techniques for Soil Data Prediction to Better Yielding of Crops
DOI:
https://doi.org/10.26438/ijcse/v6i1.203206Keywords:
Data mining, Fuzzy, neural network, decision tree, soil datasetAbstract
Yield prediction is a significant contribution for agriculture data mining to the proper choice of crops for sowing. This makes the difficulty of predicting the yielding of crops a remarkable challenge. Earlier yield prediction was performed by considering the farmer's experience on a selected field and crop. The main thing of the crop yielding is soil. This work presents the use of classification techniques to predict the soil datasets. The predicted results will express the yielding of crops. The issue of predicting the soil data is recognized as data mining technique. The soil is classified by using these techniques Naive Bayes, Decision Tree, fuzzy and neural network are used. The set of rules JRip is applied and validated on this paper using weka tool.
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