UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 5
May-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1905U39


Registration ID:
302930

Page Number

2419-2425

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Title

Performance Comparison of Machine Learning Algorithms for Crop Seed Detection. An Efficiency Analysis

Abstract

In the current generation of Artificial Intelligence ML (Machine Learning)is used for identification and detecting crop major problems. However, our work is focused on the identification of seed with accuracy. Because crop production depends on the seed. For that, identification of seed according to currently soil efficacy and weather condition is the primary need. In this paper, we take some grains with 7 different parameters and analysis of the grain. The kernel of grain is used for finding differences (detail taken from Govt. resource).In conclusion, we find the best grain for a particular soil and an algorithm which gives better prediction in this condition. For the ML algorithm, we use Logistic Regression, SVC, Decision Tree, KNN, and Random Forest for comparison purposes.

Key Words

In the current generation of Artificial Intelligence ML (Machine Learning)is used for identification and detecting crop major problems. However, our work is focused on the identification of seed with accuracy. Because crop production depends on the seed. For that, identification of seed according to currently soil efficacy and weather condition is the primary need. In this paper, we take some grains with 7 different parameters and analysis of the grain. The kernel of grain is used for finding differences (detail taken from Govt. resource).In conclusion, we find the best grain for a particular soil and an algorithm which gives better prediction in this condition. For the ML algorithm, we use Logistic Regression, SVC, Decision Tree, KNN, and Random Forest for comparison purposes.

Cite This Article

"Performance Comparison of Machine Learning Algorithms for Crop Seed Detection. An Efficiency Analysis ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.2419-2425, May-2019, Available :http://www.jetir.org/papers/JETIR1905U39.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Performance Comparison of Machine Learning Algorithms for Crop Seed Detection. An Efficiency Analysis ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp2419-2425, May-2019, Available at : http://www.jetir.org/papers/JETIR1905U39.pdf

Publication Details

Published Paper ID: JETIR1905U39
Registration ID: 302930
Published In: Volume 6 | Issue 5 | Year May-2019
DOI (Digital Object Identifier):
Page No: 2419-2425
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication
Published Paper URL :: https://www.jetir.org/view?paper=JETIR1905U39
Published Paper PDF: https://www.jetir.org/papers/JETIR1905U39

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