Performance Analysis among Different Classifier Including Naive Bayes, Support Vector Machine and C4.5 for Automatic Weeds Classification

Md Mursalin; Md. Motaher Hossain; Md. Kislu Noman

Performance Analysis among Different Classifier Including Naive Bayes, Support Vector Machine and C4.5 for Automatic Weeds Classification

Md Mursalin * §

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Md. Motaher Hossain

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Md. Kislu Noman

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shahincseru

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§ Pabna University of Science & Technology

Performance Analysis among Different Classifier Including Naive Bayes, Support Vector Machine and C4.5 for Automatic Weeds Classification

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Abstract

Weeds are often one of the biggest problems encountered by farmer in conventional agriculture. Maximum productivity of crops can be achieved by proper weeds management. Applying excessive herbicide uniformly throughout the field has an adverse effect on the environment. An automated weed control system which can differentiate the weeds and crops from the digital image could be a feasible solution for this problem. This paper demonstrates Naïve Bayes, SVM (Support Vector Machine) and C 4.5 classification algorithm for classifying the weeds and investigates the performance analysis among these three algorithms. In this study 400 sample images over five species were taken where each and every species contains 80 images. The result has shown that Naïve Bayes classification algorithm achieve the highest accuracy (99.3%) among these three classifier.

References

18 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Md Mursalin, Md. Motaher Hossain, Md. Kislu Noman, shahincseru. 1970. "Performance Analysis among Different Classifier Including Naive Bayes, Support Vector Machine and C4.5 for Automatic Weeds Classification". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 13 (GJCST Volume 13 Issue F3).

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GJCST Volume 13 Issue F3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

5 classifier C 4 herbicide image processing Naive Bayes SVM weed classification

Classification

GJCST-F Classification I.5.4

Submission ReceivedDecember 16, 2012
Peer Review Double Blind
Handling Editor
Accepted January 3, 2013
Published January 15, 2013

Version of record

v1.2

Language

English

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Article Matrices

Total Score: 144

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology

Authors: Md Mursalin,Md. Motaher Hossain, Md. Kislu Noman, Md. Shafiul Azam (PhD/Dr. count: 0)

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