Potatoes are a staple food in many countries due to their rich nutritional content, including vitamins and carbohydrates, leading to their widespread cultivation globally. As China ranks first in the world in potato cultivation area and yield, making the crop vital to national food security, mechanized harvesting has become an indispensable solution for the sustainable development of the potato industry. This paper analyzes key technologies in potato mechanized harvesting, including efficient vine removal, low-loss and obstruction-reduced digging, high-efficiency cleaning and separation, and intelligent harvesting, based on an overview of China’s potato cultivation modes, mechanized harvesting methods and equipment. Furthermore, the study identifies gaps between Chinese potato harvesting equipment and that of European and American countries in terms of harvesting performance, intelligence level, and adaptability. Key challenges are also highlighted, such as significant regional variability, the lack of high-efficiency and low-loss separation devices, and difficulties in overcoming critical “chokepoint” technologies. Finally, the paper proposes future development directions for China’s potato harvesting equipment, emphasizing 1) synergistic multi-mode development, 2) research on flexible and low-loss separation devices, and 3) the integration of intelligent technologies. This study aims to provide references for key technologies and optimized design solutions, supporting the advancement of mechanized potato harvesting and promoting sustainable and efficient practices in the potato industry.

Axial flow threshing and separating device can be one of the most important components in the grain harvester. However, the performance of the device is often confined to the blocking tendency state. In this study, a test bench for the blockage tendency was developed for the axial flow threshing and separating device. The diagnostic models of the blockage tendency were also established for the early warning of the blockage. A series of experiments were conducted to explore the effect of the material feed rate on the blockage tendency. The signals of the drum speed and the shell vibration were collected from the threshing and separating device. One speed and four acceleration sensors were utilized to determine the variation in the drum speed and shell vibration. The results indicate that there were no-load, normal, slight, and severe blockage tendencies. Specifically, a slight risk of the blockage was observed at the normal feeding rate of 0.5 kg/s, as the feeding rate increased by 20%. Furthermore, the duration of the slight blockage tendency suddenly decreased to less than 1 s when the feeding rate increased by 40%. There was a severe risk of the blockage tendency within 0.5 s. The signal adjustment also caused the drum speed with the lag and low accuracy. By contrast, the vibration signals were rich in sensitive information for the early and accurate warning of the blockage. Therefore, the Slime Mold algorithm was used to optimize the variational mode decomposition. The optimal parameters were obtained, including the mode number (K) and penalty coefficient (a). Then, the vibration signal of the sensor was decomposed into multiple intrinsic mode components (IMCs). The speed and vibration signals were verified to divide the operating intervals for each modal component under different working conditions. Nine features were extracted for each state interval, including the center frequency, Kurtosis, energy entropy, fuzzy entropy, approximate entropy, envelope entropy, envelope spectrum mean, and peak. Features 1–9 of all sensors were fused for the feature matrix (583 rows, 9 columns) and the state label matrix (583 rows, 1 column). A weighted mean of vectors algorithm was improved to reduce and then filter 9 features, in order to improve the diagnostic speed. The minimum weighted mean impacts of 233 870.46 in the features 6, 3, 4, and 7 differed from those of 53 370.36 in the features 1–9, which was the minimum of 338.20%. While the minimum weighted mean impacts of 359 675.71 in features 3, 6, 4, and 7 differed from those of 179 663.72, which was the minimum of 100.23%. The weighted mean impacts were better distinguished from the features with the high impact values. The high-sensitivity feature screening was performed to reduce the feature dimensionality. Finally, a blockage tendency of the diagnosis model was combined with the BP neural network. The feature matrix was selected as the input, while the state label matrix was selected as the output. The goodness-of-fit (R²) of the diagnostic model was greater than 0.9. The output was highly correlated with the true labels, indicating the high model reliability. The diagnostic accuracy was 92.57% under different working conditions, with a prediction speed of about 614 samples per second. The blockage tendency of the diagnosis model shared high accuracy and short time. The early warning of the blockage can be used to prevent the blockages for the high operational efficiency of the machine.

Centrifugal fans, as critical components of the cleaning device in combine harvesters, significantly influence the aerodynamic performance and operational efficiency of the device. Traditional agricultural cleaning centrifugal fans predominantly feature flat and straight blades, which exhibit suboptimal performance and low efficiency. These conventional designs often suffer from issues such as poor airflow control, excessive turbulence, and significant energy losses. Multi-blade centrifugal fans represent a promising direction for future advancements in agricultural cleaning; however, research on their application in agricultural machinery remains limited, with insufficient reference data available. This study takes the multi-blade centrifugal fan of a large foreign grain combine harvester as the prototype, and reduces its size according to the similarity principle to serve as the benchmark model fan in this paper. A combination of computational fluid dynamics (CFD) and bench experiments was employed to investigate the impact of blade inlet depth on fan performance and flow field characteristics. Firstly, a multi-blade centrifugal fan model is designed based on the similarity theory, the impeller contains 16 fan blades. The depth of the blade inlet is dimensionless to the depth-to-width ratio. Eight sets of impeller models were designed with varying blade inlet depth ranging from 39 mm to 74 mm (corresponding to the depth-to-width ratio of 0.26 to 0.54), focusing on the influence of suction commutating process and inlet efficiency by blade inlet depth of a multi-blade cleaning centrifugal fan. The performance test bench of the fan is set up to regulate the working conditions through the frequency converter, and the photoelectric speed sensor, hot film wind speed sensor and other equipment are used to collect the flow rate, full pressure and other data, so as to verify the reliability of the simulation results. The results demonstrate that optimizing the inlet depth significantly enhances fan performance. At a rated speed of 1000 r/min, increasing the inlet depth can effectively inhibit vortex generation and flow separation, and improve the suction reversal capability and air intake efficiency. The enhanced inlet depth provides a longer acceleration path for the airflow, enabling more efficient conversion of static pressure energy into kinetic energy. This results in a more stable and uniform flow field, reducing the occurrence of turbulent eddies and flow instabilities. The optimal performance is achieved when the blade depth-to-width ratio is increased to 0.46, the total pressure is increased by 11.8% to 194.42 Pa, and the full-pressure efficiency reaches 73.73%. At this time, the peak radial velocity of the airflow is increased by 21.26% compared with L_Abs=39 mm, and the area of the high velocity zone at the outlet is enlarged by 24.3%, which significantly improves the uniformity of the flow field. The pressure pulsation analysis shows that the main frequency domain pressure amplitude is minimized at this depth, and the energy loss is reduced. Velocity streamline and vortex cloud analysis reveals that optimizing the inlet depth can delay the boundary layer separation and reduce the return flow at the tongue. 32% of the impeller curvature is increased at L_Abs =69 mm, and the airflow completes the momentum conversion at 12 mm from the center disk, which realizes the high-efficiency commutation in advance compared with that of the model. The flow field visualization shows that the flow adhesion on the suction surface is enhanced and the intensity of secondary flow in the blade channel is reduced. Bench tests based on the optimal impeller model are carried out to verify the reliability of the simulation analysis. This study reveals the regulation mechanism of the blade geometric parameters on the scavenging flow field, which provides a theoretical basis and parameterization method for the design of the impeller of the combine harvester. The airflow stability and energy conversion efficiency can be improved by adjusting the inlet depth, which promotes the efficient development of agricultural centrifugal fans.