Chief EditorT. Mohapatra
Print ISSN 0367-8245
Online ISSN 0976-058X
NAAS Rating 5.20
Soil Data Analysis and Crop Yield Prediction in Data Mining using R-Tool
First Online 10-09-2020|
Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year’s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield.
Result: This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool.
- Hemageetha, N., Nasira, G.M. (2016). Analysis of the soil data using classification techniques for agricultural purpose. International Journal of Computer Sciences and Engineering. 4(6).
- Hari Ganesh, S., S Jayasudha. (2015). Data mining technique to predict the accuracy of the soil fertility. International Journal of Computer Science and Mobile Computing. 7: 330-334.
- Kanjana Devi, P., Shenbagavadivu, S. (2016). Enhanced crop yield prediction and soil data analysis using data mining. International Journal of Modern Computer Science. 4(6).
- Kumar, R.M. (2009). Crop selection method to maximize crop yield rate using machine learning technique. International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials. 1(1).
- Manjula, E., Djodiltachoumy, S. (2017). A model for prediction of crop yield. International Journal of Computational Intelligence and Informatics. 6(4): 298-305.
- Rajeswari, V., Arunesh, K. (2016). Analysing soil data using data mining classification techniques. Indian Journal of Science and Technology, 9(19).
- Samundeeswari, K., Srinivasan, K. (2018). Crop yield prediction and soil data analysis using data mining techniques in Krishnagiri district. International Journal of Computer Science and Engineering. 6(8): 49-55.
- Samundeeswari, K., Srinivasan, K. (2017). Data mining techniques in agriculture, Prediction of Soil Fertility. International Journal of Scientific and Engineering Research. 8(4): 45-51.
- Snehal, S.D. (2019). Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research in Electrical, Electronic. 2(1): 683-686. https://krishnagiri.nic.in/about-district/history.
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