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MinneApple

A Benchmark Dataset for Apple Detection and Segmentation

MinneApple

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.

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Robotic Sensor Networks Lab
https://rsn.umn.edu
Task
Object Detection
Annotation Types
Bounding Boxes
41000
Items
4
Classes
1000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Research Only
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