Machine vision for a selective broccoli harvesting robot

Pieter M. Blok, Ruud Barth, Wim Van Den Berg

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

31 Citations (Scopus)

Abstract

The selective hand-harvest of fresh market broccoli is labor-intensive and comprises about 35% of the total production costs. This research was conducted to determine whether machine vision can be used to detect broccoli heads, as a first step in the development of a fully autonomous selective harvester. A texture and color based image segmentation was used to separate the broccoli head from the background. Segmentation results were compared to a ground truth dataset of 200 images. In these images, 228 broccoli heads of varying sizes were classified by two human experts with the GrabCut algorithm. Image segmentation was evaluated by two different metrics. The first was a pixel-based spatial overlap between the ground truth classification and image segmentation, which resulted an average overlap of 93.8%. The second metric was the individual broccoli head detection and the corresponding confusion matrix. These showed a precision score of 99.5%, indicating only one false positive. The specificity was 97.9%, negative predictive value was 69.7% and overall accuracy 92.4%. In total, 208 broccoli heads were detected by the machine vision software, indicating a sensitivity score of 91.2%. The average pixel size of the non-detected heads was smaller than the pixel size of the detected heads
Original languageEnglish
Title of host publication5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016
EditorsL. Tang
PublisherIFAC
Pages66-71
Volume49
Edition16
DOIs
Publication statusPublished - 25 Oct 2016
Event5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016 - Seattle, United States
Duration: 14 Aug 201617 Aug 2016

Publication series

NameIFAC-PapersOnLine
PublisherElsevier
ISSN (Print)2405-8963

Conference

Conference5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016
Country/TerritoryUnited States
CitySeattle
Period14/08/1617/08/16

Keywords

  • Agriculture
  • Cameras
  • Computer vision
  • Image processing
  • Intelligent machines

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