Cover Image

Statistical Based Real-Time Selective Herbicide Weed Classifier

This paper deals with the development of an algorithm for real time specific weed recognition system based on Sample Variance of an image that is used for the weed classification and comparison of its result with the algorithm based on population variance. The population variance has been used befor...

Full description

Saved in:
Bibliographic Details
Main Authors: Ahmed, Irshad, Abdul Muhamin , Naeem, Muhammad, Islam, Azween, Abdullah
Format: Conference or Workshop Item
Published: 2007
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utp.edu.my/2519/1/statistical_Based_classifier.pdf
http://eprints.utp.edu.my/2519/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utp.eprints.2519
record_format eprints
spelling my.utp.eprints.25192017-01-19T08:26:49Z Statistical Based Real-Time Selective Herbicide Weed Classifier Ahmed, Irshad Abdul Muhamin , Naeem Muhammad, Islam Azween, Abdullah QA75 Electronic computers. Computer science This paper deals with the development of an algorithm for real time specific weed recognition system based on Sample Variance of an image that is used for the weed classification and comparison of its result with the algorithm based on population variance. The population variance has been used before for weed classification. The processing time for calculating population variance and sample variance for different samples is given. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on the weeds in the lab along with the prior algorithm based on population variance, which have shown that the system is very effective in weed identification and efficient than the algorithm based on population variance. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 97 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds. The algorithm developed in this paper has improved efficiency. 2007-12 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/2519/1/statistical_Based_classifier.pdf Ahmed, Irshad and Abdul Muhamin , Naeem and Muhammad, Islam and Azween, Abdullah (2007) Statistical Based Real-Time Selective Herbicide Weed Classifier. In: Multitopic Conference, 2007. INMIC 2007, 28-30, Lahore . http://eprints.utp.edu.my/2519/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ahmed, Irshad
Abdul Muhamin , Naeem
Muhammad, Islam
Azween, Abdullah
Statistical Based Real-Time Selective Herbicide Weed Classifier
description This paper deals with the development of an algorithm for real time specific weed recognition system based on Sample Variance of an image that is used for the weed classification and comparison of its result with the algorithm based on population variance. The population variance has been used before for weed classification. The processing time for calculating population variance and sample variance for different samples is given. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on the weeds in the lab along with the prior algorithm based on population variance, which have shown that the system is very effective in weed identification and efficient than the algorithm based on population variance. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 97 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds. The algorithm developed in this paper has improved efficiency.
format Conference or Workshop Item
author Ahmed, Irshad
Abdul Muhamin , Naeem
Muhammad, Islam
Azween, Abdullah
author_facet Ahmed, Irshad
Abdul Muhamin , Naeem
Muhammad, Islam
Azween, Abdullah
author_sort Ahmed, Irshad
title Statistical Based Real-Time Selective Herbicide Weed Classifier
title_short Statistical Based Real-Time Selective Herbicide Weed Classifier
title_full Statistical Based Real-Time Selective Herbicide Weed Classifier
title_fullStr Statistical Based Real-Time Selective Herbicide Weed Classifier
title_full_unstemmed Statistical Based Real-Time Selective Herbicide Weed Classifier
title_sort statistical based real-time selective herbicide weed classifier
publishDate 2007
url http://eprints.utp.edu.my/2519/1/statistical_Based_classifier.pdf
http://eprints.utp.edu.my/2519/
_version_ 1738655199772278784
score 13.211869

Similar Items