# Review of Transition from Mining 4.0 to Mining 5.0 Innovative Technologies
## 3 Review of the Transition from Mining 3.0 to 4.0
### Machine Vision and Learning in Mining 4.0  
Another digital technology domain emerging from the transition from Mining 3.0 to 4.0 is machine vision and learning, as well as robots and drones. Machine vision provides new opportunities for the robotization of mining processes, distributing engineering decisions making between people and machines (including autonomous and collaborative robots), which can be considered a bridge to Mining 5.0 [11].  
The expansion of deep machine learning based on artificial intelligence in mining gives a chance to equipment service companies, which can rely on machine-made decisions and knowledge due to the real-time collection and analysis of data required for independent decision-making by machines [101].  
Along with machine knowledge [102], machine vision has significant potential for the identification of disturbances and cracks in the rock array, using a convolutional neural network with an accuracy of prediction model of more than 85% [103]. There is also a positive experience of implementation of a random forest model of artificial intelligence for early detection of prerequisites of coal dust explosions [104].  
Another example of machine vision implementation is hyperspectral analysis of coal preparation processes using a self-control module, which made it possible to automate the process of identifying the host rock [105]. The fourth generation of flotation systems based on machine learning proved to be effective for calculating the content of a valuable component (precious minerals) in the concentrate for its complete recovery using the watershed segmentation technique for bubbles recognition, as shown in Figure 10 [106].  
Unmanned (fully robotic) equipment is a new frontier of mining machinery development [107]. Integrating data from LiDAR sensors and machine vision for processing by geometric matching algorithm allows for successfully recognizing the traffic signs in underground mines by unmanned robotic transport (979.14 matches from 1000), ensuring accident-free movement along the planned route without human intervention [108]. Monitoring of mine mechanical systems with the help of machine vision is developing towards the use of robo-inspectors that independently collect and analyze heterogeneous information (RGB (red-green-blue)-image, noise, presence and level of gases, vibrations, gamma radiation, etc.) [109]. An example of an autonomous inspector robot is shown in Figure 11 [110].  
Figure 10: Example of segmentation results using watershed algorithm (Reprinted from Ref. [106]).  
The introduction of autonomous robotic control of mine equipment will make it possible to organize monitoring of areas near working mining equipment and diagnose it in coal and ore underground and surface mines using inspector robots. Their prototypes are made in the form of a walking and jumping ground bot equipped with various sensors (Figure 12[111]).  
Aerial drone inspectors by DJI Matrice, Mavic, Phantom, etc. are promising for monitoring drilling and blasting operations in quarries, conducting preliminary, blasting, and postblast monitoring [112], as well as for the state of coal storages, dumps, and tailings, a magnetic survey of ore bodies [113; 114], and geodetic survey [115]. Drones are also used for monitoring underground mining operations--monitoring the mine atmosphere, the state of mine pillars, and the operation of equipment [116]. Along with this, the use of drones, complete with augmented reality, allows expanding your vision [117].