MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

  • Nicolai L Haeni (Creator)
  • Pravakar Roy (Creator)
  • Volkan I Isler (Creator)



We present a new dataset with the goal of advancing the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. We hope to achieve this by providing a large variety of high-resolution images acquired in orchards, together with human annotations of the fruits on the trees. Objects are labeled using polygon masks for each object instance to aid in precise object detection, localization or segmentation. Additionally, we provide data for patch-based counting of clustered fruits. Our dataset contains over 40'000 annotated object instances in 1000 images.

The counting and detection data are used to solve two different subproblems of the larger problem that is yield estimation. Yield estimation relies on accurate detection of the fruit. Since fruit can be clustered together, it is necessary to use a separate algorithm for counting (in most cases). We provide data to develop and test algorithms for both subproblems.

Funding information
Sponsorship: USDA NIFA MIN-98-G02
Date made available2019
PublisherData Repository for the University of Minnesota
Date of data productionJun 1 2015 - Sep 30 2016

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