# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
## 1 Introduction
The detection model, MDDNet, showcased superior performance, achieving a mean average precision (mAP) of 90.23% with a detection speed of 75 ms per image, underscoring the potential of such approaches in automated quality control systems. The detailed data handling process, from image acquisition to labeling and model training, provides a solid foundation for the methods employed in this case study. By adopting similar strategies in data collection and model training, initialization technique, and the application of diverse data augmentation methods, this research aims to enhance the accuracy and reliability of fruit defect detection systems. The findings of [PERSON] et al. [13] also serve as a benchmark for evaluating the effectiveness of various deep-learning models in real-world agricultural applications. The recent application of image processing and computer vision techniques has played a crucial role in food quality inspection, especially when it comes to detecting defects in fruit. [PERSON] et al. [14] discussed the use of these technologies to eliminate the subjectivity and time-consuming processes present in manual inspections. In this study, image segmentation methods such as K-means clustering were used to identify defective fruit. In addition, machine learning techniques such as KNN, SVM, and C4.5 were implemented to classify fruit as good or damaged, resulting in an efficient quality inspection and classification system. The use of methods such as Gaussian elimination to remove noise and histogram equalization to improve the quality of the images highlights the advances in automatic food quality inspection systems, reinforcing the applicability of these technologies in replacing traditional, subjective inspections. Automation in the food industry has been driven by the integration of robots into traditionally labor-intensive material handling and transport processes. The study by [PERSON] et al. [15] explores how the use of robots can increase efficiency, improve the quality and consistency of work, and reduce ergonomic risks for workers. One of the challenges faced by the food sector is the diversity of the shapes and sizes of materials, as well as their vulnerability to mechanical damage. To overcome these barriers, researchers have focused on developing robots with flexible actuators, which allow for careful adaptation during processing operations. The use of robots with end effectors specially designed for food handling, considering the mechanical properties of the materials as well as hygiene and environmental parameters, has shown great potential for improving profitability by ensuring reliable and continuous operations. The increasing implementation of computer vision in robotics has allowed robotic systems to evolve from pre-programmed cycles to more dynamic and flexible behaviors. In the study carried out by [PERSON] and [PERSON] [16], it was discussed how converting pixel coordinates into real coordinates using 2D transformations allows a robotic arm to position itself correctly for pick-and-place tasks, based on the location and orientation of detected objects. The approach uses vision sensors to map the environment and determine the coordinates (x, y) of objects, giving the robot the ability to adjust its actions in a more adaptive and efficient way. The growing demand for efficient automation methods has driven the development of intelligent robots capable of adapting to a wide range of industrial problems. [PERSON] [17] developed a flexible object recognition and handling architecture using the Robotic Operating System (ROS) framework, enabling the development of more autonomous and efficient robots. The study highlights how the integration of a three-layer system--recognition/sensors, actuators, and control--allows the robot to perform pick-and-place routines with greater precision and adaptability, even in unfamiliar environments.