MAF RODA strengthens cherry quality control with artificial intelligence and automation

The cherry season once again highlights a key factor that makes the difference in packinghouses: the ability to assess quality with the same level of precision demanded by the market in terms of uniformity, shelf life and presentation. In this context, Maf Roda reinforces its technological approach with a clear roadmap: placing Artificial Intelligence (AI) at the core of its quality systems to improve sorting consistency, reduce operational variability and deliver a homogeneous ready-to-eat product.

The company, with an international presence and experience across multiple fresh produce categories, applies these AI-driven advances across its portfolio of quality systems for fruits and vegetables, including cherries. The common objective is to equip systems with advanced visual recognition capable of interpreting patterns, even when defects are subtle or appear in combination, together with autonomous operation and ease of use.

Supported by an R&D strategy focused on automation, vision systems and data integration, Maf Roda combines two key innovation drivers in postharvest: AI and robotics. The former brings “decision-making capability” to the quality system, while the latter ensures repeatability and process continuity. The result is an installation capable of maintaining industrial throughput without compromising sorting accuracy—an essential aspect in cherries, where slight differences in color, apparent firmness or micro-defects, combined with the time pressure of a short and intense season, can determine the commercial destination of the fruit.

From machine learning to deep learning in fruit sorting

In practice, Maf Roda has been training Machine Learning models in its inspection equipment for years and has more recently incorporated Deep Learning architectures to enhance classification performance under real packinghouse conditions. This evolution enables more robust and autonomous fruit analysis, maintaining competitive speeds while improving accuracy in detecting defects and grading deviations.

Cherry inspection using machine vision in the Cherryscan G7 system for quality control

Cherry-specific solutions: sorting, grading and automation

In cherries, these advances are reflected in solutions such as Cherryscan G7, together with the CherryQS software, which represent a significant step forward in usability. The company has developed more intuitive interfaces that simplify parameter adjustment, centralizing controls on a single screen, reducing the learning curve and improving operator autonomy during the season.

Alongside AI-driven quality control, automation in cherry processing is gaining importance as a response to labor shortages, traceability requirements and the need for consistent packing performance throughout the working 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, minimizing stem interference and allowing full surface inspection, including the apical area, a particularly sensitive point for defect evaluation.

The solution is completed with a multi-format filler designed for small packages, capable of handling different presentation types—from baskets and clamshells to plastic or cardboard punnets—while maintaining a filling accuracy of ±1 fruit.

With AI as a guiding thread, Maf Roda consolidates a range of solutions where quality measurement is enhanced, operations are simplified and automation ensures continuity—key factors for delivering cherries that meet today’s market standards.

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