# Machine Vision--Moving from Industry 4.0 to Industry 5.0
## 5 Discussion
### Contribution of Machine Vision in Industry 4.0
#### 5.2.2 Practical Applications  
In what follows, results of machine vision in Industry 4.0 are referenced, covering indicative applications in the previously identified sectors. [PERSON] et al. [37] developed a machine vision system for in-line quality checks of the assembly process of a catalytic converter, enabling a paradigm of Industry 4.0 and Smart Manufacturing. [PERSON] et al. [71] presented a machine vision application for the automotive industry for detecting surface anomalies on electric motors. The authors claim that such automation systems are core features of the Industry 4.0 concept, which is led by the automotive industry. The automotive and aerospace industries are the beneficiaries of the developed framework presented by [PERSON] et al. [105]. The authors developed a framework to set up industrial inspection robots, including full coverage of the part to be inspected and simultaneous quality assessment of the part. In [119], the authors present a machine vision application for the automotive industry to automatically detect, analyze, and measure the size of the defect of automobile cluster pointers, resulting in real production lines reporting accuracy of up to 99.4%. [PERSON] et al. [116] introduced an automotive industrial implementation based on machine vision in combination with a user-friendly interface and a secure cloud environment, for solving problems related to the control and calibration in the maintenance and manufacturing of logistic racks. More generally, in the automotive sector, machine vision is employed to observe human workers (body movements, Health and Safety (H&S) compliance) as well as guide vehicles with the aim of enhancing quality and correcting errors/defects in real-time. The dominant goal is to deliver constructions with zero defects at the human system level [94]. In the manufacturing sector, [PERSON] et al. [133] presented an automatic loading/unloading system based on visual inspection. Another application of machine vision in the manufacturing sector is reported in [109]; the proposed system was based on a Cognex camera mounted on a Universal Robot (UR) robotic arm, managing to distinguish the arm from the rest of the robot's body. Despite its poor precise positioning conditions, the developed system managed to significantly support multiple industrial processes at the Industry 4.0 testbed. An application of machine vision in the industrial production of textiles was reported in [78] by [PERSON] et al. The authors developed a lightweight vision-based segmentation model capable of fabric defect detection in real time in the production line. Typical applications of machine vision in the agricultural sector within Industry 4.0 are reported in [88; 143; 144; 145; 146], focusing on mango cultivation. Mango is a fruit that needs special handling due to its short period of natural ripening on the tree, as well as its low endurance after harvest. Therefore, harvesting, storage, and transportation need to occur in a timely manner; in such cases, prediction of harvest time and yield estimation play a vital role for the correct and direct management of production. Additionally, with the use of machine vision, it became possible to count fruits or nuts on a tree, from images collected by RGB cameras mounted on ground vehicles, by applying deep learning methods for tree fruit detection, as reviewed in [147], towards a more advanced and 'intelligent' precision agriculture. Another application task in agriculture that benefits from the employment of machine vision is that of classification and ranking. Sorting a fruit into the correct category is a manual and time-consuming process that machine vision can be used to fully automate. [PERSON] et al. [89] used machine vision combined with a Convolutional Neural Network (CNN) to classify and identify rotten bananas, with a high accuracy of up to 98.3%. Another approach in agriculture is maturity level classification.