Abstract: Abstract: A direct harvester is seriously limited for the high broken kernel rate with the high moisture during harvesting. The harvest quality can significantly dominate the yield and quality. Among them, corn threshing is one of the most essential links in the corn harvesting process. The corn ears can usually be harvested, when the moisture content of the kernel is in the range of 20%-40%. Then, the corn kernel is threshed after the moisture content reduced to about 15% after drying. However, the traditional treatment cannot meet the high requirements of modern corn production, due to the long working period, high labor intensity, and high operating costs. Furthermore, it is necessary to manually adjust the operating parameters of the harvesters when observing the harvest situation, particularly under the very complicated and harsh harvesting environment in the actual production. An automatic control system is still lacking on the harvesting operating parameters for the higher productivity of agricultural machinery and equipment. In addition, the blockage in the threshing device can result in the high broken kernel rate under the uneven growth density of corn plants and the different planting agronomy, as the feeding amount of corn ears increases sharply during harvesting. Therefore, it is a high demand to timely regulate the harvesting parameters for the better operational performance of agricultural machinery and equipment. The automatic control of corn grain harvester is of great significance for the smart agriculture and digital agriculture. Fortunately, the direct harvest mode of corn kernel can be used to improve the operation efficiency with the less harvest time. This study aims to design a set of automatic control solutions to the low damage corn kernel threshing. An automatic control system was proposed to reduce the high broken kernel rate and sluggish system response of corn kernel direct harvester for the high control precision using an improved particle swarm optimization-cuckoo algorithm. Firstly, the mathematical models were established for the threshing cylinder speed-regulating motor, concave clearance regulating electric push rod motor and driving speed regulating motor, as well as the harvesting model of corn ear. Then, the automatic control logic of low damage threshing was also established, according to the influence of corn kernel harvesting parameters on the broken kernel rate. The nonlinear decreasing algorithm was used to change the particle number and inertia weight. The random walk strategy of the Cuckoo algorithm was introduced into the particle swarm optimization. The speed and position of the particle swarm were constantly updated to effectively prevent the particle swarm optimization from falling into the optimal local solution. Simulink simulation was implemented to compare the control effects of Fuzzy PID, PSO-PID, and PSO-CS Fuzzy PID algorithms on the threshing cylinder rotational speed, concave clearance, car speed, and broken kernel rate. The results showed that the improved PSO algorithm performed the best in the control accuracy, response speed, and stability. The field test of the corn kernel direct harvester was carried out to verify the improved model. The broken kernel rate was counted with the automatic control system opening and closing. The automatic control system was effectively improved the operational performance of the harvester, while the kernel broken rate was stable at about 3.80%, indicating the higher stability and accuracy of automatic control system. The findings can also provide a strong reference for the automation development of crop production machinery.