In recent years, the development of machine vision research is rapid in several areas. In order to promote the better development of machine vision research, it is necessary to clarify its development and application direction. At present, there are few reviews on the application direction of machine vision. This paper sorts out the application of machine vision in various fields, and summarizes the current application status of machine vision from four main functions: recognition, measurement, classification and detection. This paper mainly introduces the improvement of different algorithms of machine vision and its application in medical, agriculture, manufacturing and other industries, providing guidance for the selection of machine vision research direction.

The yam bean lacks accurate simulation model parameters for mechanized sowing and harvesting. The absence of parameters for the simulation model can lead to low-level mechanized design of the yam bean, which greatly affects its utilization and causes a waste of resources. This paper combines physical and simulation tests to carry out discrete elemental parameter calibration of the yam bean. The data were predictively analyzed by machine learning algorithms. A determination of basic and contact parameters of the yam bean was conducted based on physical tests. A discrete elemental model of the yam bean was developed, and angle of repose discrete elemental simulation tests were performed. The selection of simulation parameters and the identification of the optimal value range for simulation parameters were carried out by the Plackett–Burman test and the steepest climb test. Central Composite Design tests were conducted, and the results were used as a dataset for machine learning regression modeling. Then, the performances of three machine learning regression models were compared—back propagation (BP), genetic algorithm back propagation (GA-BP), and particle swarm optimization back propagation (PSO-BP). The coefficient of determination (R²), mean absolute error (MSE), and mean square error (MAE) of BP were 0.9004, 0.5204, and 0.3554, respectively. The R², MSE, and MAE of GA-BP were 0.9611, 0.2112, and 0.2809, respectively. The R², MSE, and MAE of PSO-BP were 0.9485, 0.2166, and 0.2883, respectively. It was determined that the GA-BP prediction model performed better overall. To ultimately determine the ideal set of simulation parameters, the GA-BP prediction model was examined and assessed. The GA-BP prediction model’s accuracy was validated by testing, revealing an inaccuracy of 1.22% between the rest angles measured in the simulation and the physical test. The results obtained from this research can act as a theoretical premise for further exploring the sowing and harvesting mechanisms of the yam bean.