MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

Nicolai Hani, Pravakar Roy, Volkan Isler

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In this work, we present a new dataset to advance the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. While there has been significant recent interest in solving these problems, the lack of a unified dataset has made it difficult to compare results. We hope to enable direct comparisons by providing a large variety of high-resolution images acquired in orchards, together with human annotations of the fruit on trees. The fruits are labeled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation. Additionally, we provide data for patch-based counting of clustered fruits. Our dataset contains over 41'0000 annotated object instances in 1000 images. We present a detailed overview of the dataset together with baseline performance analysis for bounding box detection, segmentation, and fruit counting as well as representative results for yield estimation. We make this dataset publicly available and host a CodaLab challenge to encourage a comparison of results on a common dataset. To download the data and learn more about the MinneApple dataset, please see the project website: http://rsn.cs.umn.edu/index.php/MinneApple. Up to date information is available online.

Original languageEnglish (US)
Article number8954630
Pages (from-to)852-858
Number of pages7
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2020


  • Agricultural automation
  • object detection
  • robotics in agriculture and forestry
  • segmentation and categorization

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