Williams, HSmith, DShahabi, JGee, TNejati, MMcGuinness, BBlack, KTobias, JJangali, RLim, HDuke, MBachelor, OMcCulloch, JGreen, RO'Connor, MGounder, SNdaka, ABurch, KFourie, JHsiao, JWerner, AAgnew, ROliver, RMacDonald, BA2024-11-202023-09-272023-111537-5110U4VH4 (isidoc)https://hdl.handle.net/10182/17843Aotearoa (New Zealand) has a strong and growing winegrape industry struggling to access workers to complete skilled, seasonal tasks such as pruning. Maintaining high-producing vines requires training agricultural workers that can make quality cane pruning decisions, which can be difficult when workers are not readily available. A novel vision system for an autonomous cane pruning robot is presented that can assess a vine to make quality pruning decisions like an expert. The vision system is designed to generate an accurate digital 3D model of a vine with skeletonised cane structures to estimate key pruning metrics for each cane. The presented approach has been extensively evaluated in a real-world vineyard as a commercial platform would be expected to operate. The system is demonstrated to perform consistently at extracting dimensionally accurate digital models of the vines. Detailed evaluation of the digital models shows that 51.45% of the canes were modelled entirely, with a further 35.51% only missing a single internode connection. The quantified results demonstrate that the robotic platform can generate dimensionally accurate metrics of the canes for future decision-making and automation of pruning.pp.31-49en© 2023 The Author(s). Published by Elsevier Ltd on behalf of IAgrEhorticultureroboticsmachine visionpruningorchardvineyardJournal Article10.1016/j.biosystemseng.2023.09.0061537-5129ANZSRC::300805 Oenology and viticultureANZSRC::400501 Architectural engineeringANZSRC::460304 Computer visionANZSRC::400701 Assistive robots and technologyANZSRC::460207 Modelling and simulationANZSRC::4099 Other engineeringhttps://creativecommons.org/licenses/by/4.0/Attribution