Owen Would
Papers
1
Total Citations
11
H-Index
1
About
Owen Would is a researcher at the intersection of agricultural robotics and self-supervised learning, whose work focuses on enabling autonomous systems to reliably detect anomalies in dynamic, real-world environments. His key research areas include representation learning, robotic perception, and precision agriculture. Would’s most notable contribution is his pioneering work on using data augmentation as a tool for self-supervised identification of fruit anomalies, demonstrating that simple, structurally peculiar augmentations can train robots to recognise atypical scenes without costly labelled data. His 2022 paper, “Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies,” has garnered 11 citations and is gaining traction among researchers seeking scalable solutions for agricultural monitoring. By reframing data augmentation as a mechanism for embedding structural peculiarity, Would has opened new pathways for autonomous robots to fully utilise unlabelled data—a critical step toward reliable, round-the-clock crop inspection. His work is particularly impactful for students and engineers interested in bridging the gap between computer vision theory and deployable robotic systems in agriculture.
Research Focus
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Top Papers
- 1