Soil Data Analysis and Crop Yield Prediction in Data Mining using R – Programming
K. Samundeeswari1, K. Srinivasan2

1K.Samundeeswari *, Department of Computer Science,, Govt. Arts College for Women, Krishnagiri, Tamil Nadu, India.
2K.Srinivasan, Department of Computer Science,, Periyar University Constituent College of Arts &Science Pennagaram, Dharmapuri, Tamil Nadu.
Manuscript received on December 12, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1857-1860 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8683019320/2020©BEIESP | DOI: 10.35940/ijitee.C8683.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Data mining is better choices in emerging research filed- soil data analysis. crop yield prediction is an important issue for selecting the crop. earlier prediction of crop is done by the experience of farmer on a particular type of field and crop. predicting the crop is done by the farmer’s experience based on the factors like soil types, climatic condition, seasons, and weather, rainfall and irrigation facilities. data mining techniques is the better choice for predicting the crop. the analysis of soil plays an important role in agricultural filed. soil fertility prediction is one of the very important factors in agriculture this research work implements to predict yield of crop, decision tree algorithm is used to find yield. the aim of this research to pinpoint the accuracy and to finding the yield of the crop using decision tree and c 4.5 algorithm is used to predict the yield of crop using r-programming and also to find range of magnesium found in the collected soil data set. this prediction will be very useful for the farmer to predict the crop yield for cultivation 
Keywords: Crop Yield Prediction, Decision Tree Algorithm, C5.0, Data Mining, R-Programming.
Scope of the Article: Data Mining