# 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 [99], maturity assessment was implemented for bunches of fresh oil palm fruits, by using machine vision and an artificial neural network (ANN) with a back-propagation algorithm, reaching 98.3% accuracy. In [135], the authors deal with the detection of citrus fruits that fall due to some disease as well as the stages of decay before the fruit falls. In cases where destructive diseases are heavily affecting plants, the detection of falling fruits, with the help of machine vision, is a method of assessing the presence and severity of the disease with a major importance. Accordingly, the detection of defective areas in a fruit or vegetable is something that machine vision can also handle. An example is that of the detection of calyx and stem scars, both for defective and healthy tomatoes, in order to improve the production process and produce products without defects, reported in [100]. In the educational sector, a machine vision-based implementation of Industry 4.0 was reported in [43]. The proposed system included simulation, VR, analytics, automation, and 3D printing to integrate a small-scale production and inspection line of 3D-printed parts. In the same context, the experimental production system [83], namely SMART, developed from the SmartTechLab laboratory based on the concept of Industry 4.0, was developed to train, update, and implement methodologies for use in modern industrial and collaborative robots. A similarly notable effort was a Learning Factory, presented in [127], based on low-cost machine vision so that the students would develop an implementation of a quality control system. Learning Factories provided a promising environment for developing the competencies required by a future workforce to implement and integrate technologies related to digitized manufacturing environments and cyber-physical systems. [PERSON] et al. [121] employed machine vision towards a useful intervention in the development of educational board games aimed at special education institutions. Specifically, teaching mathematical operations to visually impaired children with an automated tutor based on machine vision and machine learning algorithms gave 100% error detection and a 94% performance rate. The game was characterized as low-cost, fast, and non-intrusive. According to the present review, in the field of education, machine vision is employed for monitoring [70], quality control [127], and measurement issues [121]. In the construction sector, spotting holes and inspecting proper screw operation with automatic Light-Gauge Steel (LGS) machines was also an advancement of Industry 4.0; [PERSON] et al. [126], with the help of an algorithm able to detect holes and the use of a machine vision system, implemented screwing operation properly, ensuring the quality of the screwing 59% more accurately than the fastest available algorithm that time. [PERSON] and [PERSON] [128] proposed a machine vision and machine learning system in the metallurgical industry, to improve product quality, helping to detect surface defects such as scratches, spots, shallow pits, edge defects, etc., as well as processing the elements such as size, diameter, thickness, height, etc. An application of machine vision in the meat industry is that of cutting meat. By making use of laser scanners enabling a 3D cutting image, with the help of weight control (to adjust volume and density) of a computer and machine vision techniques, the cutting process can be implemented with higher precision. For this specific process, the cutting knife managed to perform 700 cuts/min with an accuracy of \(\pm 0.5\%\)[101].