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Data Mining Technique to Predict Annual Yield for Major Crops


Rahul Bandu Ombale , NBN Sinhgad school of engineering,ambegaon(bk),pune; Rahul Ombale, NBN Sinhgad school of engineering,ambegaon(bk),pune; Rajshekhar Borate, NBN Sinhgad school of engineering,ambegaon(bk),pune; Sagar Ahire, NBN Sinhgad school of engineering,ambegaon(bk),pune; Manoj Dhawade, NBN Sinhgad school of engineering,ambegaon(bk),pune


Data Mining, Crop Analysis, Yield Prediction, Clustering, K-Means, Linear Regression


The complexity of predicting the best crops is highly due to unavailability of proper knowledge discovery in crop knowledge base which affects the quality of prediction. However, Clustering is an important step in mining useful information. There are multiple clustering methods such as partition, hierarchical, model based grid-based. Constrained-based which make this task complicated due to problems related to optimization and noise. In this paper k-means clustering algorithm issued in solving the partition problem which led to select for performance evaluation and linear regression algorithm in order to get good quality of clusters for crop prediction. This project aimed to apply new data mining techniques on dataset to establish meaningful relationships can be found.

Other Details

Paper ID: IJSRDV4I31092
Published in: Volume : 4, Issue : 3
Publication Date: 01/06/2016
Page(s): 1835-1837

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