NDLI: Automatic classification of olives for oil production using computer vision

Content Provider	IEEE Xplore Digital Library
Author	Martinez Gila, D. Aguilera Puerto, D. Gamez Garcia, J. Gomez Ortega, J.
Copyright Year	2015
Description	Author affiliation: Group of Robot., Autom. & Comput. Vision, Univ. of Jaen, Jaén, Spain (Martinez Gila, D.; Aguilera Puerto, D.; Gamez Garcia, J.; Gomez Ortega, J.)
Abstract	One of the most important parameters in olive oil production is the correct reception and classification of olive fruits in batches before starting the oil extraction process. This work proposes an automatic inspection system based on computer vision to classify automatically different lots of olives for oil production when the milling process starts. The classification is based on the differentiation, on line, between ground and tree olives. For this purpose, the samples of olives have been obtained by picking berries directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image processing algorithms have been employed and lineal classification techniques such as Fisher Discriminant Analysis. The system has reached good classification results distinguishing between soil and tree olives batches with success ratios of 100%.
Starting Page	1651
Ending Page	1656
File Size	1930320
Page Count	6
File Format	PDF
ISBN	9781479978007
DOI	10.1109/ICIT.2015.7125334
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-17
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Soil Production Histograms Computer vision Image color analysis Cameras Principal component analysis Olive oil extraction process Olives Classification Computer Vision
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

Olive batches automatic classification in mill reception using computer vision

Automatic determination of peroxides and acidity of olive oil using machine vision in olive fruits before milling process

Application of computer vision and Support Vector Machines to estimate the content of impurities in olive oil samples

Automatic sushi classification from images using color histograms and shape properties

Automatic quality evaluation of fruits using Probabilistic Neural Network approach

Automatic Auroral Detection in Color All-Sky Camera Images

Learning spatial relationships from 3D vision using histograms

Graph-based dimensionality reduction for KNN-based image annotation

Inspection of bottles crates in the beer industry through computer vision

Automatic classification of olives for oil production using computer vision

Similar Documents

Olive batches automatic classification in mill reception using computer vision

Automatic determination of peroxides and acidity of olive oil using machine vision in olive fruits before milling process

Application of computer vision and Support Vector Machines to estimate the content of impurities in olive oil samples

Automatic sushi classification from images using color histograms and shape properties

Automatic quality evaluation of fruits using Probabilistic Neural Network approach

Automatic Auroral Detection in Color All-Sky Camera Images

Learning spatial relationships from 3D vision using histograms

Graph-based dimensionality reduction for KNN-based image annotation

Inspection of bottles crates in the beer industry through computer vision

Automatic classification of olives for oil production using computer vision