## 1 Introduction
* Models based on the YOLO8 and YOLO9 architectures are proposed to effectively perform object detection for the three identified classes of strawberries. By addressing these objectives, the study aims to enhance the efficiency and accuracy of defect strawberry classification, ultimately improving the quality and sustainability of fruit processing operations. The novelty of this research is multifaceted and can be discussed from several angles. Firstly, it marks a significant step towards enhancing overall agricultural productivity by applying the SA concept within the strawberry processing industry. Developing and implementing a new hardware and software infrastructure enables quick reporting of defects in alignment with ISO 9001:2015 [21] and IFS FOOD guidelines [22]. This infrastructure supports the efficient management of quality and defects, which is crucial for maintaining high standards in strawberry production. Secondly, introducing a dataset advances the current state of research in object detection systems, specifically targeting the classification of strawberries using YOLOv8 n, s, m, l, and x and YOLOv9 m, c, t, s and e architectures. This approach enhances the accuracy of defect detection and addresses the limitations of traditional manual methods, which are often labor-intensive and inefficient. This comprehensive approach ensures that the models perform well under diverse conditions, reflecting real-world scenarios. Finally, integrating edge devices, web applications and machine vision algorithms improves quality control measures in agriculture. This integration exemplifies the application of modern technologies to address the challenges faced by the agricultural sector, particularly in strawberry processing. The subsequent sections of the paper explore the integration of SA and Quality 4.0 concepts, focusing on innovative technologies such as the Internet of Things, cloud computing, machine vision, and data analytics, which are essential for maintaining agricultural competitiveness (Section 2.1). The role of machine vision in managing defects in strawberries is discussed, covering detection and classification processes to ensure continuous quality control (Section 2.2). The strawberry storage process is described in detail, highlighting steps from handpicking to packaging to maintain quality and safety (Section 2.3). The design of software components for SA, including a tailored quality control system using computer vision, is detailed, showcasing how various technologies are employed (Section 2.4). Data acquisition and processing are explained using a Zenodo dataset and YOLOv8 and YOLOv9 models for defect detection (Section 2.5). The performance of these models is assessed using standard metrics (Section 2.5.4). The results section illustrates the practical application of the system in real-life scenarios, such as strawberry classification by defects (Section 3). The significance of defect reporting and the development of a modular software solution for defect identification and reporting in SA are highlighted in the discussion (Section 4). Finally, the importance of SA-driven quality control in achieving superior standards and addressing the challenges of defect identification is emphasized in the conclusion (Section 5).