Surveying apple orchards with a monocular vision system

Pravakar Roy, Volkan Isler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

We present computer vision algorithms to collect yield related information in an apple orchard using images collected from a single camera. The goal of our system is to give farmers the capability to use their phones or digital cameras to record images and obtain yield related parameters. There are two challenges in this setup which necessitate novel methods: (i) It is very difficult to generate dense matches using standard image features. (ii) The constrained geometry of the setup causes existing structure from motion algorithms to fail. We present a novel piecewise incremental structure from motion technique to register and reconstruct the apples which is used for extracting count and diameter information. We validate our approach by presenting results from multiple field trials.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
PublisherIEEE Computer Society
Pages916-921
Number of pages6
ISBN (Electronic)9781509024094
DOIs
StatePublished - Nov 14 2016
Event2016 IEEE International Conference on Automation Science and Engineering, CASE 2016 - Fort Worth, United States
Duration: Aug 21 2016Aug 24 2016

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2016-November
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
CountryUnited States
CityFort Worth
Period8/21/168/24/16

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  • Cite this

    Roy, P., & Isler, V. (2016). Surveying apple orchards with a monocular vision system. In 2016 IEEE International Conference on Automation Science and Engineering, CASE 2016 (pp. 916-921). [7743500] (IEEE International Conference on Automation Science and Engineering; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/COASE.2016.7743500