Computer vision and artificial intelligence promise to revolutionize horticulture with automation and more objective measurements of traits of agronomical importance. While many algorithms are already published in this domain, the transferability of this literature to the techno providers or the final users is often limited due to the absence of sharing of the software or of the data set used to train these softwares. Some major initiatives to address the lack of reproducibility, in the plant domain at large, have so far been limited to plant models (Arabidopsis) or major crops (wheat). Horticulture is therefore waiting for more similar initiatives. In this work, we present an annotated data set on apple detection that we make publicly available. This data set produced in collaboration with a group of European variety testing offices on several sites is associated with a baseline algorithm (deep learning) already providing reasonable results. The challenge carried out by this data set is typical of an orchard environment with a background creating a major clutter with the targeted foreground. The test of new algorithms is made accessible via the deployment of a data challenge related to this data set. We will present the result of the data challenge which is open during the academic year 2021-2022 to master students in data science.