## 2 Materials and Methods
### Initial Assumptions and Objectives for the Present Study  
The strawberry storage process involves several meticulously designed steps to ensure the fruits remain high quality and safe for consumption. Strawberries are handpicked at optimal ripeness to prevent physical damage and ensure peak quality. Following harvest, the fruits undergo preliminary sorting to remove any visibly damaged, rotten, or unripe specimens [34]. The strawberries are then thoroughly cleaned and washed to eliminate dirt, pesticides, and other impurities, ensuring their hygiene and safety. Detailed sorting follows, where strawberries are categorized by size, color, and quality, either manually or with machines, to select the best fruits for subsequent processing. These sorted strawberries are then rapidly frozen through flash freezing, preserving their freshness, texture, and nutritional value by forming small ice crystals. Finally, frozen strawberries are packed into protective, often vacuum-sealed or airtight, containers labelled with relevant product information, including the freezing and expiration dates. This comprehensive process guarantees that the strawberries maintain their quality and nutritional integrity from harvest to consumption. Driven by the outlined assumptions, activities in the strawberry storage process and the imperatives of quality, particularly concerning defect reporting, this study aims to leverage technologies from the SA toolkit, such as edge devices and machine vision YOLO algorithms. These technologies could be applied for preliminary and detailed sorting activities, utilizing high-resolution imaging and AI algorithms to categorize strawberries based on predefined quality criteria. The study seeks to optimize the entire strawberry storage and processing workflow through these innovations, ensuring adequate product quality and consumer satisfaction in the SA context. The overarching goal is to demonstrate how the development of an accessible cloud-based solution utilizing JavaScript ECMAScript 2024, Python 3.11.5, and MySQL 8.0.35 databases holds promise for facilitating real-time defect identification, thereby fostering engagement, continuous improvement, and evidence-based decision-making. Design of General Software Components, Their Interrelationships, and Available Technologies for Smart Agriculture Consolidation  
The schematic presentation of software components and their interconnections is illustrated in Figure 1 based on [PERSON] et al. [35]. This figure provides insight into the potential technologies that could be employed to realize various software components and their interplay across distinct tiers within the software architecture. Additionally, Section 2.5 introduces a tailored software infrastructure, aligning with contemporary technological advancements. Figure 1 illustrates the potential technologies encompassed within the developed solution. The proposed software integrates computer vision techniques utilizing YOLOv8 n, s, m, l, and x and YOLOv9 c, m, s, t, and e algorithms, coupled with edge camera deployment positioned above the narrowing conveyor belt transporting strawberries. For training the YOLOv8 and YOLOv9 algorithms, a Lenovo ThinkPad P16 (Lenovo, Quarry Bay, Hong Kong) with an Intel Core i7-12850 HX processor (16 \(\times\) 1.5-4.8 GHz) (Intel, Santa Clara, CA, USA) and NVIDIA RTX A2000 Laptop GPU (8 GB VRAM) (NVIDIA, Santa Clara, CA, USA) was utilized (step 1). This configuration supports computational tasks required for training machine-learning models, ensuring the readiness of the algorithms for defect detection and strawberry data management. For this application, a Dahua 2 MP network bullet camera (Dahua Technology, Hangzhou, China) with full-color technology was employed (1/2.8\" CMOS with progressive scan). It operates effectively in low light conditions and provides a maximum resolution of 1920 \(\times\) 1080.