## 1 Introduction
These object detection algorithms are relatively simple and computationally efficient, but they are not as accurate and robust as deep-learning-based object detection algorithms. In contrast to traditional approaches, deep-learning-based models employ convolutional neural networks (CNNs) [10]to automatically extract features from raw image data. CNNs are designed to capture hierarchical representations of visual information, enabling them to learn complex patterns and structures present in images [11]. By leveraging these learned features, deep learning models exhibit increased robustness to variations in object appearance, such as changes in lighting conditions, viewpoints, and occlusions [12]. The capability of deep-learning-based object detection models to generalize well to new images and improve detection performance compared to hand-crafted feature-based methods has been harnessed effectively for fruits and vegetables detection and recognition. In this context, we have specifically fine-tuned the parameters of two versions of the YOLO algorithm, namely YOLOv5n [13] and YOLOv7 [14], capitalizing on their inherent capacity to adapt and excel in detecting fruits and vegetables across various types of images. YOLO, or You Only Look Once, is a single-stage object detector, as opposed to the commonly used two-stage object detectors such as R-CNN [15], Fast R-CNN [16], and Faster R-CNN [17]. YOLO algorithm, adopts a unique methodology of processing images by making predictions in a single pass [18]. It analyzes the entire input image as a whole and directly predicts the presence and spatial coordinates of objects within it. This approach distinguishes YOLO from traditional two-stage object detection methods, resulting in computational efficiency by eliminating the need for region proposal and subsequent refinement steps [18]. By processing the image only once, YOLO achieves real-time object detection capabilities, making it well-suited for resource-constrained environments [19]. In this paper, we propose a computer vision-based weighing scale system for the real-time detection and measurement of fruits and vegetables. The system comprises a camera, a loaded, an hx711 module, an Arduino microcontroller, and a laptop. The camera captures real-time video, which is then processed by a deep learning model, such as YOLOv5n or YOLOv7, running on the laptop to make real-time predictions of the object. The Arduino microcontroller interfaces the hx711 module with the loadcell, which is used to measure the weight of the objects. Serial communication is used to transfer the weight measurements to the laptop. A python script has been written in order to create a graphical user interface (GUI). The GUI has everything on it, such as particular item real time video, its name, weight, and price with respect to their weight. Using graphical user interface (GUI) you can add different items to your bill once you weigh them by just clicking the \"add\" button. At the end, you can receive your bill in printed form when you complete your order by just clicking the \"print\" button that has been shown on the graphical user interface (GUI). The system is designed to be efficient, reducing labor costs when multiple weighing scales are utilized, as the shopkeeper needs to enter the price of the object only once. This intelligent weighing scale would empower shopkeepers to serve customers more efficiently, enabling multiple customers to be attended to simultaneously. The reduction in labor requirements would also alleviate the burden on the shopkeepers, allowing them to focus on other aspects of their business. Our proposed system has the potential to improve the efficiency and accuracy of small-scale retail operations. This study is a step towards modernizing small-scale retail operations and future research could be focused on the implementation of this system in real-world scenarios and its impact on the industry.