Development of a Computer Vision-Based Method for Sizing and Boat Error Assessment in Olive Pitting Machines

Table olive pitting machines (DRRs) are essential in the agri-food industry but face significant limitations that constrain their performance and compromise process reliability. The main defect, known as the “boat error”, results from improper olive orientation during pitting, leading to bone fragmentation, pulp damage, and potential risks to consumer safety. Traditional quality control methods, such as the use of flotation tanks and expert sensory evaluation, rely on destructive sampling, are time-consuming, and reduce overall productivity. To address these challenges, this study presents a novel computer vision (CV) system integrated into a commercial DRR machine. The system captures high-speed images of Gordal olives (Olea europaea regalis) just before pitting; these are later analyzed offline using a custom MATLAB application that applies HSV-based segmentation and morphological analysis to quantify the olive size and orientation. The method accurately identifies boat error cases based on angular thresholds, without interrupting the production flow or damaging the product. The results show that 97% of olives were correctly aligned, with only 1.1% presenting critical misorientation. Additionally, for the first time, the system allowed a detailed evaluation of the olive size distribution at the machine inlet, revealing an unexpected proportion of off-caliber olives. This contamination in sizing suggests a possible link between calibration deviations and the occurrence of boat errors, introducing a new hypothesis for future investigation. While the current implementation is limited to offline analysis, it represents a non-destructive, low-cost, and highly precise diagnostic tool. This work lays the foundation for a deeper understanding of DRR machine behavior and provides a framework for future developments aimed at optimizing their performance through targeted correction strategies.