NDLI: A Vision-based Classifier in Precision Agriculture Combining Bayes and Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Tellaeche, A. BurgosArtizzu, X.P. Pajares, G. Ribeiro, A.
Copyright Year	2007
Description	Author affiliation: UNED, Madrid (Tellaeche, A.)
Abstract	One important objective in precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. In order to reach this goal, two major factors need to be considered: 1) the similar spectral signature, shape and texture between weeds and crops; 2) the irregular distribution of the weeds within the crop's field. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. The proposed system involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and the weeds. From these attributes, a hybrid decision making approach, under a Bayesian framework determines, if a cell must be or not sprayed. The hybrid approach uses the support vector machines for computing the prior probability in this Bayesian framework. This makes the main finding of this paper. The method performance is compared against other available strategies.
Starting Page	1
Ending Page	6
File Size	1803128
Page Count	6
File Format	PDF
ISBN	9781424408290
DOI	10.1109/WISP.2007.4447561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-03
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Support Vectors Machines Shape Decision making Crops Spraying Bayesian classifier Support vector machines Image segmentation precision agriculture Bayesian methods Support vector machine classification Agriculture Parzen's windows weed detection
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Vision-based Classifier in Precision Agriculture Combining Bayes and Support Vector Machines

On Combining Support Vector Machines and Fuzzy K-Means in Vision-based Precision Agriculture

Weed detection system using support vector machine

A classifier ensemble based on performance level estimation

Bayesian classification and active learning using lp-priors. Application to image segmentation

Traffic Sign Detection and Pattern Recognition Using Support Vector Machine

Tablets Vision Inspection Approach Using Fourier Descriptors and Support Vector Machines

Weed Recognition Based on Erosion and Dilation Segmentation Algorithm

Automated Detection of Root Crowns Using Gaussian Mixture Model and Bayes Classification

A Vision-based Classifier in Precision Agriculture Combining Bayes and Support Vector Machines

Similar Documents

A Vision-based Classifier in Precision Agriculture Combining Bayes and Support Vector Machines

On Combining Support Vector Machines and Fuzzy K-Means in Vision-based Precision Agriculture

Weed detection system using support vector machine

A classifier ensemble based on performance level estimation

Bayesian classification and active learning using lp-priors. Application to image segmentation

Traffic Sign Detection and Pattern Recognition Using Support Vector Machine

Tablets Vision Inspection Approach Using Fourier Descriptors and Support Vector Machines

Weed Recognition Based on Erosion and Dilation Segmentation Algorithm

Automated Detection of Root Crowns Using Gaussian Mixture Model and Bayes Classification

A Vision-based Classifier in Precision Agriculture Combining Bayes and Support Vector Machines