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About

Jalil Shahabi is a robotics researcher focused on transforming specialty crop production through autonomous systems. His work centers on agricultural robotics, computer vision, and machine learning for high-value horticultural crops like wine grapes and apples. Shahabi’s major contribution lies in developing perception and decision-making algorithms that enable robots to perform skilled, seasonal tasks traditionally requiring human expertise. His highly cited 2023 paper on modelling wine grapevines for autonomous robotic cane pruning (31 citations) addresses New Zealand’s critical labour shortage by teaching robots to identify and select optimal canes for pruning—a complex decision based on vine architecture and health. In parallel, his work on mapping apple fruitlets (5 citations) demonstrates robotic systems that can accurately measure crop load at the individual tree level, enabling data-driven thinning decisions. These contributions have direct impact on the agricultural sector, with potential to reduce labour dependency while improving precision and consistency. Shahabi’s research sits at the intersection of robotics, agronomy, and computer vision, offering practical solutions that could reshape how New Zealand’s $2 billion wine and apple industries operate.