A Survey on Rice Crop Yield Prediction in India Using Improved Classification Technique

Authors

  • Kolin Sukhadia  PG Scholar, Department of Computer Engineering, Government Engineering College Modasa, Modasa, Gujarat, India
  • M. B. Chaudhari  Head of Department, Department of Computer Engineering, Government Engineering College Modasa, Modasa, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT1951122

Keywords:

AdaBoost Technique, BayesNet Classifier, Rice Crop Yield Prediction, Kharif Season

Abstract

India is an agricultural country. Agriculture is the important contributor to the Indian economy. There are many classification techniques like Support Vector Machine(SVM), LADTree, Naïve Bayes, Bayesnet, K Nearest Neighbour(KNN), Locally Weighted Learning(LWL) on rice crop production datasets. They have some drawbacks like low accuracy and more errors. To achieve more significant result, To increase classification accuracy and reducing classification errors, our research uses classification method Bayesnet based adaboost will be proposed in work. Rice crop yield depend on environment’s parameters like Rainfall, minimum temperature, average temperature, Maximum temperature, Vapour Pressure, potential evapotranspiration, reference crop evapotranspiration, cloud cover, wet day frequency for the kharif season. our dataset containing these environmental parameters for accurate prediction of Rice crop yield.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-02-28

Issue

Volume 5, Issue 1, January-February-2019

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Research Articles

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How to Cite

[1]
Kolin Sukhadia, M. B. Chaudhari, " A Survey on Rice Crop Yield Prediction in India Using Improved Classification Technique , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.501-507, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT1951122