Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

Rick van Essen*, Ben Harel, Gert Kootstra, Yael Edan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

This paper presents a rule-based methodology for dynamic viewpoint selection for maturity classification of red and yellow sweet peppers. The method makes an online decision to capture an additional next-best viewpoint based on an economic analysis that considers potential misclassification and robot operational costs. The next-best viewpoint is selected based on color variations on the pepper. Peppers were classified into mature and immature using a random forest classifier based on principle components of various color features derived from an RGB-D camera. The method first attempts to classify maturity based on a single viewpoint. An additional viewpoint is acquired and added to the point cloud only when it is deemed profitable. The methodology was evaluated using leave-one-out cross-validation on datasets of 69 red and 70 yellow sweet peppers from three different maturity stages. Classification accuracy was increased by 6% and 5% using dynamic viewpoint selection along with 52% and 12% decrease in economic costs for red and yellow peppers, respectively, compared to using a single viewpoint. Sensitivity analyses were performed for misclassification and robot operational costs.

Original languageEnglish
Article number4414
JournalApplied Sciences (Switzerland)
Volume12
Issue number9
DOIs
Publication statusPublished - 27 Apr 2022

Keywords

  • dynamic viewpoint selection
  • economic analysis
  • harvesting robot
  • machine vision
  • maturity classification
  • next-best-view planning
  • sweet peppers

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