Computer Vision (CV) has proven to be a powerful tool for automation in agri-food industrial processes, offering high-precision solutions tailored to specific working conditions. Recent advancements in Artificial Neural Networks (ANNs) have revolutionized CV applications, enabling systems to autonomously learn and optimize tasks. However, ANN-based approaches often require complex development and lengthy training periods, making their implementation a challenge. In this study, we explore the use of the Segment Anything Model (SAM), a pre-trained neural network developed by META AI in 2023, as an alternative for industrial segmentation tasks in the table olive (Olea europaea L.) processing industry. SAM’s ability to segment objects regardless of scene composition makes it a promising tool to improve the efficiency of olive pitting machines (DRRs). These machines, widely employed in industrial processing, frequently experience mechanical inefficiencies, including the “boat error,” which arises when olives are improperly oriented, leading to defective pitting and pit splinter contamination. Our approach integrates SAM into n CV workflow to diagnose and quantify boat errors without designing or training an additional task-specific ANN. By analyzing the segmented images, we can determine both the percentage of boat errors and the size distribution of olives during transport. The results validate SAM as a feasible option for industrial segmentation, offering a simpler and more accessible solution compared to traditional ANN-based methods. Moreover, our statistical analysis reveals that improper calibration—manifested as size deviations from the nominal value—does not significantly increase boat error rates. This finding supports the adoption of complementary CV technologies to enhance olive pitting efficiency. Future work could investigate real-time integration and the combination of CV with electromechanical correction systems to fully automate and optimize the pitting process.

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.

El Ciclo de Mejora en el Aula (CIMA) se ha integrado en la asignatura de Proyectos de Ingeniería Agrícola para 172 estudiantes, centrándose en la gestión de proyectos agrícolas y el manejo del software de presupuestos Presto. Este enfoque pedagógico, centrado en el estudiante, fomenta la participación activa y el desarrollo de competencias claves como el trabajo en equipo, la toma de decisiones y el pensamiento crítico. El contenido del programa abarca 8 horas y se centra en problemas esenciales como la elaboración de presupuestos y el uso de Presto. Apoyado en el aprendizaje activo y la participación mediante tecnologías emergentes, el CIMA incluye actividades como cuestionarios y debates. Se emplea una metodología definida para cada sesión, con actividades y objetivos claros. La evaluación se efectúa mediante cuestionarios antes y después del curso, usando escaleras de aprendizaje para evaluar el avance en la comprensión de presupuestos y Presto. Cuestionarios de 20 alumnos elegidos al azar permiten medir el entendimiento desde lo básico hasta lo avanzado, garantizando un seguimiento preciso del aprendizaje práctico.