The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics

This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature...

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Autores principales:	Edmund J. Sadgrove, Greg Falzon, David Miron, David W. Lamb
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	agricultural robotics computer vision drone stationary camera trap ensemble extreme learning machine Agriculture S
Acceso en línea:	https://doaj.org/article/1d20c262acf94199aa78eb70bba4fe45
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id	oai:doaj.org-article:1d20c262acf94199aa78eb70bba4fe45
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spelling	oai:doaj.org-article:1d20c262acf94199aa78eb70bba4fe452021-11-25T16:10:29ZThe Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics10.3390/agronomy111122902073-4395https://doaj.org/article/1d20c262acf94199aa78eb70bba4fe452021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2290https://doaj.org/toc/2073-4395This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture.Edmund J. SadgroveGreg FalzonDavid MironDavid W. LambMDPI AGarticleagricultural roboticscomputer visiondronestationary camera trapensembleextreme learning machineAgricultureSENAgronomy, Vol 11, Iss 2290, p 2290 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	agricultural robotics computer vision drone stationary camera trap ensemble extreme learning machine Agriculture S
spellingShingle	agricultural robotics computer vision drone stationary camera trap ensemble extreme learning machine Agriculture S Edmund J. Sadgrove Greg Falzon David Miron David W. Lamb The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
description	This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture.
format	article
author	Edmund J. Sadgrove Greg Falzon David Miron David W. Lamb
author_facet	Edmund J. Sadgrove Greg Falzon David Miron David W. Lamb
author_sort	Edmund J. Sadgrove
title	The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
title_short	The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
title_full	The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
title_fullStr	The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
title_full_unstemmed	The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
title_sort	segmented colour feature extreme learning machine: applications in agricultural robotics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1d20c262acf94199aa78eb70bba4fe45
work_keys_str_mv	AT edmundjsadgrove thesegmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics AT gregfalzon thesegmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics AT davidmiron thesegmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics AT davidwlamb thesegmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics AT edmundjsadgrove segmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics AT gregfalzon segmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics AT davidmiron segmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics AT davidwlamb segmentedcolourfeatureextremelearningmachineapplicationsinagriculturalrobotics
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The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics

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