# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
## 1 Introduction
The use of ROS offers standardization in robotic operations, making the system applicable to various industrial scenarios, as was demonstrated in the European Robotics Challenge (EUROC) for logistics and handling on production lines. Emerging technologies such as computer vision and artificial intelligence (AI) are revolutionizing the food industry by promoting process automation based on the massive use of real-time data and predictive models. [PERSON] et al. [18] provide a comprehensive analysis of the impact of these technologies on agriculture and food processing, exploring how solutions based on AI and computer vision can help farmers improve production efficiency and sustainability. Their study highlights the potential of these technologies to promote sustainable food production, which is essential for meeting future demands. The review also addresses the challenges and recommendations for implementing such technologies in real-time agricultural environments, emphasizing the need for global policies and significant investments to enable the transition to smart agriculture. The use of automation technologies in agriculture has been key to increasing quality, economic growth, and productivity in several countries. [PERSON] and [PERSON] [19] discuss how assessing the quality of fruit and vegetables, especially in terms of appearance--a crucial sensory characteristic--impacts market value and consumer choice. Traditionally, the grading and sorting of these products is performed manually, but this process is susceptible to inconsistencies, variability, and the influence of external factors, as well as being time-consuming and costly. As a result, there is a growing need for automated sorting and separation systems based on computer vision. This study presents a detailed review of the main image processing methods used to assess the quality of fruit and vegetables, covering stages such as pre-processing, segmentation, and feature extraction and classification, based on parameters such as colour, texture, size, shape, and defects. In this way, the advancement of this technology offers an alternative to traditional methods, such as manual inspection and chemical analyses, which are commonly used to assess the quality of fruits and vegetables. These traditional methods often rely on subjective visual evaluations or destructive testing, such as measuring sugar content through refractometry or chemical assays, which can be time-consuming, labor-intensive, and costly. In contrast, the implementation of a computer vision system can reduce operational costs through simpler installation and easy integration into specific environments, while providing a non-destructive and objective means of quality assessment. In addition to their use in the food industry, advances in digital solutions have been widely introduced into manufacturing systems to make them smarter and more efficient. In the context of industrial waste management, intelligent systems enable data collection and analysis to improve decision-making and optimize remote process management. A striking example is the computer vision system proposed by [PERSON] et al. [20], which uses convolutional neural networks (CNN) and deep learning techniques to automatically estimate the fill level of scrap metal containers. Validated in a case study in the copper tube production industry, the system demonstrated an accuracy of between 77.5% and 95%, providing an effective solution for autonomous monitoring of these containers and reducing dependence on manual inspections or intrusive mechanical systems. Computer vision and deep learning technologies have also shown a great impact in the agricultural sector. One example is the work of [PERSON] et al. [21], who developed an intelligent robotic system for harvesting tea shoots. Using algorithms such as YOLO-v3 and semantic segmentation techniques, the system was designed to identify the buds and determine the exact picking points, achieving an average accuracy of 83% in extracting these points.