Vision-Based Apple Counting and Yield Estimation

Pravakar Roy, Volkan Isler

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We present a novel method for yield estimation in apple orchards. Our method takes segmented and registered images of apple clusters as input. It outputs number and location of individual apples in each cluster. Our primary technical contributions are a representation based on a mixture of Gaussians, and a novel selection criterion to choose the number of components in the mixture. The method is experimentally verified on four different datasets using images acquired by a vision platform mounted on an aerial robot, a ground vehicle and a hand-held device. The accuracy of the counting algorithm itself is 91 %. It achieves 81–85% accuracy coupled with segmentation and registration which is significantly higher than existing image based methods.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Advanced Robotics
PublisherSpringer Science and Business Media B.V.
Pages478-487
Number of pages10
DOIs
StatePublished - 2017

Publication series

NameSpringer Proceedings in Advanced Robotics
Volume1
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

Bibliographical note

Funding Information:
Acknowledgments. This work is supported in part by NSF grant # 1317788, USDA NIFA MIN-98-G02 and the MnDrive initiative.

Publisher Copyright:
© 2017, Springer International Publishing AG.

Keywords

  • Apple Orchard
  • Expectation Maximization
  • Gaussian Mixture Model
  • Greedy Method
  • Minimum Description Length

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