The cherry season, with its short window and intense work rhythms, has become one of the best indicators of how far innovation has progressed inside packing facilities. The need for near‑surgical precision in sorting —minimal differences in colour, firmness or micro‑damage— requires systems capable of handling high volumes without losing finesse in selection. In this context, Maf Roda has taken another step forward in its technological strategy: extending Artificial Intelligence across its entire range of quality‑control solutions for fruits and vegetables, including cherries.
The company, with international presence and experience across multiple categories, has defined a clear roadmap: integrating AI models trained under real warehouse conditions to improve grading consistency and reduce operational variability. The goal is to enable the system to interpret complex patterns, even when defects are subtle or appear in combination, while keeping the interface simple for operators.
This progress is built on two pillars: AI and robotics. The former provides judgement and decision‑making capability; the latter ensures repeatability and continuity. Together, they allow industrial speeds without sacrificing precision —a critical factor in cherries, where even small deviations can determine the commercial destination of a batch.
Maf Roda has spent years training Machine Learning models in its inspection equipment, but the recent incorporation of Deep Learning architectures marks a qualitative leap. These models allow fruit to be analysed with greater robustness, adapt more effectively to natural variability and increase defect‑detection accuracy, all while maintaining competitive throughput.
Focus on cherries
In cherries, this evolution is reflected in solutions such as Cherryscan G7, paired with the CherryQS software, which introduces significant improvements in usability. The interface has been redesigned so that parameter adjustments can be made from a single screen, reducing the learning curve and increasing operator autonomy during the season.
Automation is also gaining ground as a response to labour shortages and the need to ensure consistent packing throughout the day. In this area, the Cherryway IV grader stands out, designed for gentle fruit handling and maximum visibility during inspection. Its four‑movement rotation system positions the cherry transversely, minimising peduncle interference and allowing full‑surface viewing, including the apical area —a key point for defect assessment.
The offering is completed with a multi‑format filler designed for small packages —baskets, clamshells or plastic/cardboard punnets— maintaining a filling accuracy of ±1 fruit, a feature especially valued in markets where presentation is decisive.
With AI as the common thread, Maf Roda consolidates a portfolio of solutions where quality is measured more precisely, operations become simpler and automation ensures continuity. A combination of factors that, in a category as demanding as cherries, translates into a more homogeneous product aligned with the standards the market now expects.
