Phenotyping Problems of Parts-per-Object Count

Faina Khoroshevsky, Stanislav Khoroshevsky, Oshry Markovich, Orit Granitz, Aharon Bar-Hillel

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

3 Scopus citations


The need to count the number of parts per object arises in many yield estimation problems, like counting the number of bananas in a bunch, or the number of spikelets in a wheat spike. We propose a two-stage detection and counting approach for such tasks, operating in field conditions with multiple objects per image. The approach is implemented as a single network, tested on the two mentioned problems. Experiments were conducted to find the optimal counting architecture and the most suitable training configuration. In both problems, the approach showed promising results, achieving a mean relative deviation in range of 11 % – 12 % of the total visible count. For wheat, the method was tested in estimating the average count in an image, and was shown to be preferable to a simpler alternative. For bananas, estimation of the actual physical bunch count was tested, yielding mean relative deviation of 12.4 %.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 Workshops, Proceedings
EditorsAdrien Bartoli, Andrea Fusiello
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783030654139
StatePublished - 1 Jan 2020
EventWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12540 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom


  • Deep neural networks
  • Object detection
  • Part counting
  • Yield estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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