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Depth imaging-based detection of muskmelon plant for phenotyping in the greenhouse

  • Lei Li
  • , Qin Zhang
  • , Danfeng Huang
  • Shanghai Jiao Tong University
  • Washington State University Pullman

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

Abstract

As the rapid development of plant breeding and genomics, more effective and reliable phenotyping data has become the bottleneck for modern genetic crop improvement. In this article, we assess the potential of depth imaging system for phenotyping muskmelon. An original algorithm to detect muskmelon based on fusion images of RGB image and Depth image was proposed from complex backgrounds of greenhouse. Two types of features are extracted: 1) features from depth information; and 2) statistical features derived directly from RGB images. From muskmelon plant detected, various measurements of muskmelon phenotype involving for number of leaves, size of leave, size of fruits will be presented to demonstrate the practical interest of such imaging systems.

Original languageEnglish
Title of host publicationAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2014, ASABE 2014
PublisherAmerican Society of Agricultural and Biological Engineers
Pages2858-2867
Number of pages10
ISBN (Electronic)9781632668455
StatePublished - 2014
Externally publishedYes
EventAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2014, ASABE 2014 - Montreal, Canada
Duration: 13 Jul 201416 Jul 2014

Publication series

NameAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2014, ASABE 2014
Volume4

Conference

ConferenceAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2014, ASABE 2014
Country/TerritoryCanada
CityMontreal
Period13/07/1416/07/14

Keywords

  • Depth camera
  • Depth segmentation
  • Muskmelon
  • Phenotyping

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