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Volume 6 Issue 10

October 2020

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title

AGRICULTURAL ANALYSIS USING DATAMINING AND MACHINE LEARNING

Author(s):

G.Elizabeth rani

Keywords:

Agriculture analysis, Data mining, K-means clustering Algorithm, SVM, KNN.

Abstract

Agriculture is the most importance in India. The latterday technologies can change the situations of farmers and decision makers in agricultural field. To analyze the dataset of agriculture, Python is used to predict the crop production. We used Jupyter Notebook which is a data mining tool to predict the crop production. In FAOSTAT dataset contains the parameter are precipitation, temperature, reference crop, area, evapotranspiration, production and yield for the season from January to December for the years 2000 to 2018. The data mining techniques are like K-means clustering, KNN, Bayesiam network algorithm and SVM are used. Using these algorithms, where high accuracy can be achieved.Precisian agricultural is a key component. Precision Agriculture Classification on FAOSTAT Dataset is perform using Artificial Neural Networks.

Other Details

Paper ID: IJSARTV
Published in: Volume : 6, Issue : 5
Publication Date: 5/1/2020

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