TOMRA Neon optimizes optical grader efficiency by removing more than 95% of clusters and more than 90% of green and red berries, the company says. It has a throughput capacity of up to 500 berries per second and can maintain a speed of up to 280 berries per second even when fruit removal is as high as 40%. To identify, differentiate and remove unwanted clusters, undersize fruit and unripe fruit, TOMRA Neon harnesses artificial intelligence. It was prototype-tested over two-and-a-half years in varied machine-harvested conditions in North America and New Zealand. 

The Deep Learning technology of the Spectrim X series brings to the apple market pre-trained models to teach computers how to process data, such as complex patterns in photos. Spectrim X series assesses thousands of high-resolution, multi-channel fruit images every second. Then it cross-references what it sees with data to make grading decisions to meet precise market demands. This data has been captured from TOMRA machines across the globe and labelled by TOMRA’s Data Science Team. During 18 months of testing in the USA, and New Zealand, Spectrim X series showed a leap forward in performance compared to its predecessor.

While the Spectrim X series integrates LUCAi technology, existing Spectrim customers who grade apples can use the LUCAi upgrade package. New "plug-and-play" Deep Learning models can detect and classify apple defects, such as splits and punctures across multiple varieties. LUCAi also allows customers to adjust the severity of grading parameters to cater for seasonal dynamics and market preferences. And the pre-trained intelligence of this upgrade is customizable.