## 2 Materials and Methods
### Agricultural Products Defects Management  
Many studies have focused on fruit detection but have overlooked the problem of detecting fruits at multiple stages. The DSE (Detail-Semantics Enhancement) YOLO method was proposed by [PERSON] et al. [28] to detect multi-stage strawberries. In the DSE-YOLO method, the DSE module was designed to detect small fruits and distinguish different stages of fruit development with higher accuracy. The results show that DSE-YOLO can almost accurately detect each stage of strawberry development in natural conditions, providing an important theoretical basis for automatic picking and monitoring systems [13]. [PERSON] et al. [29] highlight the challenge of swiftly and accurately identifying strawberry growth conditions and maturity for automated orchard management robots, particularly for operations like automatic pollination, fertilization, and picking. Strawberries, with their short ripening period and heavy overlap and shading, pose significant challenges to traditional detection methods regarding efficiency and effectiveness.  
To address this, the authors propose SDNet (Strawberry Detect Net), an algorithm based on the YOLOX model. Their findings address the challenge of accurately monitoring strawberry fruit growth states in complex environments, offering crucial insights for advancing unmanned farming and SA technologies. [PERSON] et al. [30] present an improved method for recognizing small target strawberries using the YOLOv8n model, addressing the challenges of detecting smaller strawberries and reducing misdetections caused by complex backgrounds in strawberry images. [PERSON] et al. [31] emphasize the importance of accurately identifying strawberry seedling flowers and fruits in greenhouse environments for automated flower and fruit thinning, which enhances efficiency and reduces labor costs in cultivation. The authors propose an algorithm that integrates a Swin Transformer prediction head on the high-resolution feature map of YOLO v7 to leverage spatial location information, enhancing detection accuracy for small targets amidst scenes with similar colors and occlusions. These algorithms primarily focus on enhancing the accuracy and efficiency of detecting strawberry fruits and flowers in various environments and developmental stages.  
Thus, machine vision YOLO models could play a crucial role in identifying defects in strawberries. These models may be adept at detecting various types of defects present on the surface of strawberries, ranging from physical damage to discoloration. Typical defects include mechanical damage, physiological disorders, internal defects, morphological and pathological disorders, and customer and supplier returns [32]. Once a defect is detected, these models can further classify them based on their severity or type, allowing for a nuanced understanding and differentiation between defective and healthy strawberries. The continuous monitoring capabilities of machine vision models ensure ongoing quality control throughout the strawberry production and processing stages.  
By monitoring strawberries, these models can promptly identify any potential defects, enabling intervention to prevent further deterioration and maintain product integrity. This entails the imperative implementation of quality control mechanisms, facilitating the early detection of defects and the reduction in production waste [33]. Analysis of the relevant literature [33] reveals distinct objectives for the solution outlined in this paper across several categories: (1) affordability and suitability for the SA; (2) incorporation of computer vision (YOLOv8 n, s, m, l and x) modules, and aligning with the analytic modules.