## 1 Introduction  
Modern agricultural companies prioritize producing higher-quality products and meeting changing consumer needs to stay competitive. Achieving these goals requires constant monitoring, control, and improvement of processing procedures for both organic and conventional products. This demands the implementation of effective quality management practices and modern technology [1]. Despite the integration of quality management initiatives, the dynamic nature of agricultural markets presents significant obstacles that surpass the effectiveness of traditional quality management strategies. These challenges notably include the classification of fruit products and potential defects they may exhibit. Additionally, the traditional manual approach in this field is associated with repetitive, physically demanding work. For example, quality control and food classification may take place in environments with low temperatures and other challenging conditions. As a cornerstone of modern agriculture, the strawberry industry greatly emphasizes product quality and profitability [2, 3]. A significant criterion in assessing strawberry grade is the presence of defects, such as mold [4, 5], which stands as one of the more prevalent issues in the strawberry domain. Strawberry defect detection relies heavily on manual labor, characterized by extended work hours, elevated costs, and suboptimal efficiency. Particularly concerning strawberry mold, traditional detection methods are time-consuming and ineffective. In recent years, there has been an increase in the use of machine vision technologies in agriculture, leading to the development and deployment of various neural network architectures [6; 7]. [PERSON] et al. [8] created a system using You Only Look Once (YOLO) to detect citrus diseases, such as Citrus Canker and Citrus Greening, allowing for better disease management. [PERSON] et al. [9] introduced Ag-YOLO, an inexpensive object detection system specifically designed for precise spraying in precision agriculture using unmanned aerial vehicles (UAVs). This system addresses the challenges of effective pesticide application in small fields and rugged terrains, such as mountainous areas, by providing a cost-effective and adaptable onboard computer vision solution. [PERSON] et al. [10] developed a pest detection system for hazelnut orchards, achieving 94.5% average precision using a YOLO-based CNN and deploying the system on an NVIDIA Jetson Xavier for real-time processing. [PERSON] et al. [11] focused on enhancing productivity and reducing operational costs in crop harvesting systems. They proposed an optimized YOLO-based object detection model, YOLO-P, specifically tailored for automated crop harvesting systems. The model aims to detect and localize fresh fruit bunches, grabbers, and palm trees in palm oil plantations under various environmental conditions, providing essential visual information for the development of intelligent automated harvesting systems. The model shows a high accuracy of 98.91% in identifying fresh fruit bunches of various maturities, indicating its robustness and effectiveness for real-world applications in palm oil plantations. In a study by [PERSON] et al. [12], the use of YOLO models for fruit detection and load estimation in an orange orchard was investigated, emphasizing the importance of accurate yield estimation for effective market and resource planning in precision agriculture. The findings highlight the effectiveness of YOLO models in providing practical solutions for detecting and estimating orange fruit yields, suggesting a combined imaging approach to improve accuracy for varying canopy densities, with an overall yield estimation error of +9.19%. [PERSON] et al. [13] introduced DSE-YOLO (Detail-Semantics Enhancement YOLO) as a solution for multi-stage strawberry detection, crucial for estimating crop yields and facilitating robotic harvesting in modern agriculture.