Apple yield mapping using hyperspectral machine vision

V. Alchanatis, O. Safren, O. Levi, V. Ostrovsky

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

11 Scopus citations

Abstract

For orchard growers, it is important to estimate the quantity of fruit on the trees at different stages of their growth. This study proposes a method of automatically detecting apples in digital images that can be used for automating the yield estimation of apples on trees at different stages of their growth by means of machine vision. This investigation concentrates on estimating yield of green varieties of apples. To achieve this goal, hyperspectral imaging was applied. A multistage algorithm was developed which utilizes PCA and ECHO as well as machine vision techniques. The overall correct detection rate was 87.0% with an overall error rate of 14.9%.

Original languageEnglish
Title of host publicationPrecision Agriculture 2007 - Papers Presented at the 6th European Conference on Precision Agriculture, ECPA 2007
Pages555-562
Number of pages8
StatePublished - 1 Dec 2007
Event6th European Conference on Precision Agriculture, ECPA 2007 - Skiathos, Greece
Duration: 3 Jun 20076 Jun 2007

Publication series

NamePrecision Agriculture 2007 - Papers Presented at the 6th European Conference on Precision Agriculture, ECPA 2007

Conference

Conference6th European Conference on Precision Agriculture, ECPA 2007
Country/TerritoryGreece
CitySkiathos
Period3/06/076/06/07

Keywords

  • Apples
  • Hyperspectral
  • Image processing
  • Machine vision
  • Yield mapping

ASJC Scopus subject areas

  • Agronomy and Crop Science

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