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

Author(s)
Sadgrove, Edmund J
Falzon, Greg
Miron, David
Lamb, David W
Publication Date
2021-11
Abstract
This article belongs to the Special Issue Data-Driven Agricultural Innovations.
Abstract
<p>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.</p>
Citation
Agronomy, 11(11), p. 1-16
ISSN
2073-4395
Link
Publisher
MDPI AG
Rights
Attribution 4.0 International
Title
The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics
Type of document
Journal Article
Entity Type
Publication

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