Conference Agenda

Optical sorting is widely used in industrial quality control, yet conventional rule-based vision systems often struggle when quality cues are subtle, heterogeneous, or hard to formalize. We present an industry data science case study on deploying deep learning for real-time optical sorting of hazelnuts, driven by the practical need to grade product quality from fine-grained appearance characteristics under strict throughput and latency constraints.

The talk traces the path from an early prototype to an industrialized system that has been transferred into a market-ready product and is operated in practice. We summarize the end-to-end solution: multi-camera image acquisition, a supervised learning pipeline built on a representative labeled dataset, domain-specific preprocessing and targeted data augmentation, and a neural image classifier designed for on-premise inference.

We emphasize industrial aspects that proved central for making the system operational: formalizing expert grading into maintainable classes, managing imbalance and borderline cases during data preparation, data labeling and training, and setting decision thresholds based on acceptance criteria. We then cover deployment realities for industrial environments, e.g. latency, throughput, robustness, and the interface between the ML component and machine control.

Finally, we describe how the solution was productized and extended beyond hazelnuts to additional crops, enabling new application scenarios and market opportunities for the customer. We conclude with practical considerations for lifecycle management and periodic re-calibration.