The segmented colour feature extreme learning machine: Applications in agricultural robotics

Edmund J. Sadgrove, Greg Falzon, David Miron, David W. Lamb

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number2290
Number of pages16
JournalAgronomy
Volume11
Issue number11
DOIs
Publication statusPublished - 12 Nov 2021

Keywords

  • Agricultural robotics
  • Computer vision
  • Drone
  • Ensemble
  • Extreme learning machine
  • Feature mapping
  • Object classification
  • Stationary camera trap

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