Abstract

3D information provides unique information about shape, localisation and relations between objects, not found
in standard 2D images. This information would be very beneficial in a large number of applications in agriculture such as fruit picking, yield monitoring, forecasting and phenotyping. In this paper, we conducted a
study on the application of modern 3D sensing technology together with the state-of-the-art machine learning
algorithms for segmentation and detection of strawberries growing in real farms. We evaluate the performance
of two state-of-the-art 3D sensing technologies and showcase the differences between 2D and 3D networks
trained on the images and point clouds of strawberry plants and fruit. Our study highlights limitations of the
current 3D vision systems for the detection of small objects in outdoor applications and sets out foundations for
future work on 3D perception for challenging outdoor applications such as agriculture.