## 1 Introduction  
In recent years, the rapid transformation driven by digitization and automation has brought about profound changes in various facets of our lives. Significantly, advancements in technology, particularly in the realm of artificial intelligence (AI), have served as the catalyst for this paradigm shift. Digitization involves the conversion of analog information into digital formats, while automation leverages technology to execute tasks with minimal human intervention. The integration of AI applications has played a pivotal role in harnessing the full potential of digitization and automation. By utilizing AI, digitization enables the analysis of unstructured data, encompassing images, audio, and text, thereby extracting valuable insights from these sources. For instance, AI-powered image recognition algorithms have facilitated the automatic tagging and categorization of images. Simultaneously, AI's impact on automation is equally momentous, empowering machines to perform tasks that were traditionally dependent on human intelligence. These intelligent machines execute complex tasks with precision and swiftness, bolstering work efficiency and diminishing labor costs. By leveraging digitization and AI-powered automation, we can bring about a transformative change in the small-scale retail sector of Pakistan. In Pakistan, where a significant proportion of the population belongs to a low socio-economic status [1], small-scale retail outlets play a pivotal role in fulfilling their daily consumption needs. These outlets offer goods in quantities that are affordable and accessible to this segment of the population, given their limited purchasing power, unlike large-scale supermarkets that primarily cater to bulk purchases. However, the pricing of goods in these small-scale retail outlets relies on conventional weight scales, which involve a manual and time-consuming process. To address the inherent inefficiencies and high labor demands associated with this traditional method, it becomes imperative to embrace digitization and automation. Drawing inspiration from the advancements in artificial intelligence (AI), we propose the adoption of an intelligent weighing scale in small-scale retail outlets across Pakistan. This innovative scale would incorporate a deep learning algorithm capable of real-time detection and recognition of various fruits and vegetables, accurately weigh the items, and display their corresponding prices on a user-friendly graphical interface. Such implementation would significantly enhance operational efficiency and reduce labor requirements, consequently benefiting both the retailers and the customers in these outlets. Deep learning has seen a significant rise in recent years, which can be primarily attributed to the progress in digital technologies and availability of large data sets [2]. Digital technologies such as high-performance computing and powerful GPUs have facilitated the training of deep neural networks on large data sets [2]. This combination of powerful computing resources and large data sets has enabled deep learning to achieve state-of-the-art performance in a wide range of applications of computer vision [3]. Deep learning models have played a crucial role in the development of real-time object detection systems. Object detection is a computer vision task focused on recognizing and localizing objects present in images or videos. It involves identifying the presence of specific objects and determining their spatial coordinates within the given visual data [4]. The objective of object detection is to enable machines to perceive and understand the content of images or video frames [5]. Traditional object detection algorithms, such as Harris Corner Detector [6], Scale-Invariant Feature Transform (SIFT) [7], and Speeded Up Robust Features (SURF) [8; 9], rely on hand-crafted features and heuristics to detect objects.