Abstract

In recent years, there has been an increasing interest in using robotics and autonomous systems for fruit farming. These systems use detection and segmentation algorithms to provide computers with a way to interpret and interact with plants, thus allowing them to automatize assessment and harvesting tasks. This type of algorithm requires a dataset to train a machine learning algorithm, but most types of fruits lack a publicly available dataset. We present Cherry CO, a novel, high-resolution dataset for cherry detection and segmentation. This dataset contains 3,006 labeled images that were acquired in a cherry plantation. The images were taken in various conditions to account for most challenges in this environment. Each cherry was manually labeled to account for location, shape and classes, taking into account aspects such as ripeness, health state and location. This letter presents a detailed description of data acquisition, annotation specifications and statistical analysis of the dataset. In addition, we trained and evaluated several Deep Neural Networks to provide a benchmark for cherry detection, achieving high performance with most networks, most notably YOLOv7. The dataset and evaluation software are provided for research purposes.