MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

TL;DR

MinneApple is a comprehensive, publicly available dataset with over 41,000 annotated apple images designed to improve and benchmark fruit detection, segmentation, and counting methods in orchard environments.

Contribution

This work introduces a large, annotated dataset for apple detection and segmentation, enabling standardized evaluation and comparison of fruit analysis algorithms.

Findings

Baseline detection and segmentation results provided

Dataset facilitates yield estimation research

Challenge hosted to promote method development

Abstract

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, 000 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 comparison of results on a common dataset. To download the data and learn more about MinneApple please see the project website: http://rsn.cs.umn.edu/index.php/MinneApple. Up to date information is available online.