New blueberry machine that separates, scans, and evaluates softness

GP Graders, a New Zealand-based sorting and grading equipment manufacturer, has launched a new machine that combines hygienic engineering with high-speed imaging to assess fruit quality. The innovation can also gauge firmness, historically a difficult variable to measure at scale.

The company states the new device’s sealed imaging unit captures multiple angles of each berry using a combination of color, monochrome, and near-infrared sensors. This generates detailed images and data to detect defects like bruising, cracks, dehydration, and size inconsistencies.

GP Graders’ solution to gauge firmness 

To evaluate berries’ firmness, the company’s new machine uses near-infrared data and image analysis to identify subtle firmness variations. This allows packers to sort fruit by appearance, expected shelf life, and texture performance.

The machine developer reports that the system’s softness detection outperforms other market solutions, positioning it as a leader in blueberry grading technology.

Operators can adjust grading settings and monitor performance through the device’s interface. Built-in analytics deliver real-time insights into product quality and sorting results.

GP Graders’ solution to the sanitation conundrum

Blueberries pose particular machine sanitation challenges due to their delicate skin and juice residue, the company explained. However, this is not an issue for the GP Graders’ latest innovation. 

The machine is built according to 3-A SSI sanitary principles and designed with rounded stainless-steel structures to prevent water and residue buildup. It was also created to withstand frequent cleaning—often daily—with high-pressure water and sanitizing chemicals.

The machine is available in 4-, 8-, 12-, and 16-lane systems, letting packhouses scale capacity to their needs. A 16-lane configuration can process between two and three about 2–3 tons per hour and integrates directly with automatic punnet and clamshell fillers, maintaining throughput without compromising grading accuracy.

*All images are referential. 

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