The cherry campaign highlights a key factor in modern warehouses: the ability to assess quality to meet the uniformity, shelf life, and presentation standards that markets demand. In this context, Maf Roda aims to integrate AI into its quality systems to improve classification consistency, minimize operational variability, and produce a homogeneous, ready-to-eat product.
© MAF RODA AGROBOTIC
The company, which has an international presence and expertise in a range of fresh fruit categories, incorporates these AI innovations into its quality systems for fruits and vegetables, including cherries. The goal is to use advanced visual recognition technology to analyze patterns and identify defects, even subtle or combined ones, within an autonomous and user-friendly quality management system.
Supported by an R&D strategy focusing on automation, vision, and data integration, Maf Roda uses AI and robotics for post-harvest processing. AI enhances quality assessment criteria, while robotics ensures repeatability and continuous operation. This results in an installation capable of maintaining an industrial pace without sacrificing selection precision, which is crucial in cherry processing. In cherries, minor variations in color, firmness, or micro-damage, coupled with time constraints arising from short, intensive campaigns, may influence the ultimate market destination.
© MAF RODA AGROBOTIC
Maf Roda has been training machine learning models in its inspection equipment for years and has recently integrated deep learning architectures to boost classification capacity in real warehouse scenarios. This development allows for more robust and autonomous fruit analysis, keeping speeds competitive and improving accuracy in defect and category deviation detection.
In cherry, these advancements are reflected in solutions like Cherryscan G7 and CherryQS software, which represent a major improvement in usability. Maf Roda has developed more intuitive interfaces that make it easier to adjust key parameters on a single screen, thereby reducing the learning curve and enhancing operator autonomy in the field.
Alongside advances in AI-driven quality control, cherry automation is becoming increasingly important due to labor shortages, the need for traceability, and the demand for consistent packaging throughout the day. The Cherryway IV sizer is notable here, as it is designed to gently handle fruit while enhancing visibility during inspection. Its four-movement rotation system places the cherry in a transverse position, minimizing interference from the stalk and allowing the entire surface to be observed, including the apical area, a particularly sensitive point in the evaluation of defects. The proposal for the sector is completed with a multi-format filler for small packages, capable of working with different types of presentation, such as baskets, clamshells, or plastic or cardboard tubs, while maintaining a filling accuracy of ±1 fruit.
With AI, Maf Roda consolidates solutions that improve quality, simplify operations, and ensure automation, crucial factors to delivering cherries that meet today's market standards.
© Roda Ibérica S.L.For more information:
Maf Roda Group
www.mafroda.com
