# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
## 1 Introduction  
In 2015, a study carried out by The Food and Agriculture Organization (FAO) estimated that 30% of the food produced for human consumption worldwide is lost or wasted somewhere along the food supply chain [1]. By 2050, when the world population is expected to reach the 9.1 billion mark, demand for food will increase by 70% [2]. Food loss and waste have significant negative impacts on food security, the economy, and the environment. Developing efficient solutions to reduce food loss and waste involves, for example, recognizing the integrity and quality of fruit and vegetables between the different stages of the food supply chain. The quality of fruit and vegetables is related to their degree of ripeness. This degree can be determined by a combination of physical and chemical attributes, which determine acceptability to consumers [3]. Five different attributes define the quality of fruit andvegetables: visual quality (size, shape, color, and defects), texture, flavor (taste and aroma), nutritional values, and safety [4]. In general, the quality of fruits and vegetables is assessed by sensory and subjective determination, using scoring scales, whose quality levels are characterized by a brief description and exemplary images of the product in question [5]. In addition, conventional destructive methods are used to measure the chemical and physical attributes of fruits and vegetables to support sensory evaluation. Analytical and destructive techniques are widely used, although they are expensive and time-consuming, and have a negative impact on the environment and the product. These techniques require sophisticated equipment and careful sample preparation [6]. According to [PERSON] et al. [7], natural food products generally vary in shape and are sensitive to damage, which represents a considerable difficulty when it comes to handling by robotic systems, since the diversity of food products, characterized by significant variations in size, texture, weight, susceptibility to damage, color and shape, makes it challenging or even impossible to handle them efficiently with traditional grippers, regardless of whether these are on/off or vacuum grippers [7]. In this case, flexible manipulators offer good deformation and ductility, ensuring safety during interaction with the environment, and they are often chosen as the final gripping devices for sorting products in agricultural robots, as highlighted in [8; 9; 10]. As stated by [PERSON] et al. [11], soft manipulators with the fin effect (FRE) present a simpler and more economically viable approach to grasping objects of various shapes. The biomimetic structure eliminates the need for inbuilt actuation in the fingers, thus facilitating passive adaptation to the surface of an object upon contact. Since the introduction of the FRE finger by automation company Festo and its commercial product in 2016 [12], extensive research has inspired numerous innovative applications. Computer vision represents an ideal non-destructive technology for assessing the quality of fruits and vegetables. This technology can be implemented for industrial line applications, representing a reliable and suitable option for this environment. Recent advancements in computer-vision-based fruit quality recognition have demonstrated significant improvements in accuracy and efficiency, particularly when coupled with optimized machine learning models deployed on embedded systems. The work by [PERSON] et al. [13] is representative in this regard, presenting a rapid and precise method for detecting multiple types of defects using a multispectral imaging system combined with an enhanced YOLOv3-tiny model. This study uses comprehensive data acquisition and processing methodologies, which include the collection of 428 potato samples--both defective and non-defective--and the use of traditional data augmentation techniques to expand the training dataset significantly.