  Selective robotic harvesting is a promising technological solution to address
labour shortages which are affecting modern agriculture in many parts of the
world. For an accurate and efficient picking process, a robotic harvester
requires the precise location and orientation of the fruit to effectively plan
the trajectory of the end effector. The current methods for estimating fruit
orientation employ either complete 3D information which typically requires
registration from multiple views or rely on fully-supervised learning
techniques, which require difficult-to-obtain manual annotation of the
reference orientation. In this paper, we introduce a novel key-point-based
fruit orientation estimation method allowing for the prediction of 3D
orientation from 2D images directly. The proposed technique can work without
full 3D orientation annotations but can also exploit such information for
improved accuracy. We evaluate our work on two separate datasets of strawberry
images obtained from real-world data collection scenarios. Our proposed method
achieves state-of-the-art performance with an average error as low as
$8^{\circ}$, improving predictions by $\sim30\%$ compared to previous work
presented in~\cite{wagner2021efficient}. Furthermore, our method is suited for
real-time robotic applications with fast inference times of $\sim30$ms.

Computer Vision and Pattern Recognition;Artificial Intelligence