MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

Abstract

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.