Agriculture is one of the oldest human activities, dating back over 11,000 years, and has always evolved alongside technological progress. Innovations in tools and machinery have historically aimed to reduce the physical and mental fatigue of farmers. Nevertheless, many operations, such as pruning and fruit harvesting, are still predominantly carried out manually, either because they require high precision or because existing solutions are far from being effective. Current research increasingly focuses on robotic and automated solutions, whose effectiveness, however, depends strongly on the ability of machines to perceive and interpret complex natural environments. In the case of fruit harvesting, the focus of this thesis, state-of-the-art perception technologies typically focus on detecting fruits while neglecting the surrounding structures that are equally crucial for robotic navigation and manipulation.