# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
## 5 Conclusions  
This article analyzes the development and application of advanced technologies in the food sector, with a specific focus on computer vision and the use of robotics. The central problem addressed was the need to continuously improve automation and precision in food industry processes, using non-destructive technologies that are easy to implement and have economic advantages. The main objectives included the implementation of computer vision systems for fruit detection, focusing on tomatoes. In this study, models were developed for the detection and classification of tomatoes, and each model was tested using two different versions. The models for tomato detection achieved a mean average precision (mAP) of 67.54% for four classes and 64.66% for two classes, with an Intersection over Union (IoU) ranging from 0.5 to 0.95. To facilitate collaboration between a robotic arm and computer vision, a rectangular work area was defined. After conducting 640 manipulation tests, the system demonstrated a reliable operational area of 262 \(\times\) 250 mm\({}^{2}\). This determination was based on 390 successful tests, accounting for 60% of the total tests. The developed system in this study holds potential for the food industry, particularly in replacing the human worker for the repetitive task of fruit processing and quality control, while increasing its efficiency and accuracy. By integrating computer vision and collaborative robotics, the system automates the detection and handling of fruits such as tomatoes, reducing reliance on manual labour and minimizing damage during processing. In fruit sorting facilities, the system can automatically classify fruits based on size, ripeness, and quality, ensuring consistent product standards and reducing waste by accurately sorting fruits according to specific quality criteria. The system's high accuracy in detecting tomatoes enables integration into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that the produce is adequately sorted so that each quality tier is processed accordingly. This application enhances the speed and precision of quality control processes, leading to improved product selection accuracy. For future work, we suggest using other robot models, especially delta robots, to study the speed of action compared to a robotic arm. Another suggestion is to create a database, instead of relying on nonspecific available databases, to optimize the trained model with images typical of its use. Picking moving fruits would also be an interesting development, as it would allow integration in sorting production lines, bringing the idea of this project closer to real applicability in industry. Finally, creating a cloud database that directly communicates with the programmed system would enable the verification of the reliability of each received load by cataloging the number of defective fruits. This would enhance production efficiency and improve the selection of suppliers in the future. Conceptualization, E.V.F., M.L.A. and P.D.G. ; methodology, E.V.F., L.L., M.L.A., R.A., N.P. and P.D.G. ; validation, E.V.F., L.L., M.L.A. and P.D.G. ; formal analysis, E.V.F., L.L., M.L.A., R.A., N.P. and P.D.G. ; investigation, E.V.F. ; resources, P.D.G. ; data curation, E.V.F., L.L. and M.L.A. ; writing--original draft preparation, E.V.F., L.L., M.L.A. and P.D.G. ; writing--review and editing, E.V.F., M.L.A., R.A., N.P. and P.D.G. ; supervision, M.L.A. and P.D.G. ; project administration, P.D.G. ; funding acquisition, P.D.G. All authors have read and agreed to the published version of the manuscript.