Three sorting lines:
male, female & return.
Fully adaptable: you control the balance between speed and precision.
We are driving a paradigm shift in the industry. HatchFlex goes beyond traditional chick sorting to introduce a technological innovation designed to address modern digital challenges predictably, reliably, and efficiently.
The process is structured into three lines: males, females and return. The dedicated return line is a unique HatchFlex feature that automatically redirects chicks not clearly identified as male or female for re-sorting.
HatchFlex does not just fit your process; it’s designed for your business continuous evolution.
Owner
Hatchery Manager
Promotional video
AI Sorting Chicks by Gender is an automated hatchery process that uses artificial intelligence and image analysis to classify chicks by gender. HatchFlex manages uncertain cases through a return line, supporting an achieved >99% accuracy in the sorting process.
AI chick sexing improves hatchery efficiency by automating classification, reducing manual workload and supporting high-throughput operations. HatchFlex is designed to integrate into hatchery flows and process up to 25,000 chicks per hour.
Static image capture improves AI gender sorting by analyzing each chick in a stable position. This enhances image quality and delivers unmatched consistency in results, helping HatchFlex achieve >99% accuracy—unlike motion-based systems where movement compromises clarity and reliability.
HatchFlex manages low-confidence chick classification—which occurs when the device has doubts about whether a chick is male or female and is not 100% sure—with three sorting lines: male, female, and return. These uncertain cases are automatically redirected and reanalyzed instead of forcing a decision, helping protect accuracy, throughput, and operational control.
HIPRA’s HatchFlex is a modular AI Gender Sorting Device built around static image capture, three sorting lines and configurable precision. It adapts to each hatchery’s size and flow, delivering an achieved >99% accuracy with scalable implementation.
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