Corn harvesting mode has gradually changed from ear harvesting to direct grain harvesting. In view of the problems of high moisture content in corn harvesting in China, such as the rates of broken grains (BGR) and uncleared grains (UGR) being too high, a new single longitudinal axial threshing cylinder was designed, which mainly included a cylinder spindle, a spiral feeding inlet, a T-type rasp bar, a separating straight rod, and a spiral extracting rod. Firstly, the three states of grain during the threshing process and the key influence factors of threshing and force analysis of corn ears in the threshing device were analyzed, then the structure of the threshing cylinder was designed, and its parameters were determined by theoretical analysis results. The arrangement mode of threshing elements adopted a combination of a T-type rasp bar, a separating straight rod, and a spiral extracting rod with a 6-head spiral pattern and an arrangement step of 250 mm. Secondly, the arrangement step of threshing elements was determined by discrete element method solution (DEM) simulation; the result showed that the average movement velocity was 55.04 m/s and the threshing time was 6–8 s. Finally, a multiple factors experiment of the threshing device was carried out, and the result showed that the order of the effect factors of the BGR and UGR was cylinder rotational speed > concave clearance > feed amount. When cylinder rotational speed was 309.17 r/min, concave clearance was 35.48 mm, and the feed amount was 6.13 kg/s. The verification experiment result showed that the BGR and UGR were 1.24% and 1.33%, respectively, which meet standard requirements. The research results could provide a reference for the design of a high moisture content grain threshing device and combine harvester.

At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate.

The driving tasks of a maize harvester are exhausting because of the varying soil, maize conditions, and the long working time. Operators must adjust and optimize the internal settings of the harvester to modify the working parameters and reduce the harvest loss. In this paper, an intelligent control system for maize picking harvest was investigated for automatic adjustment and minimization of maize picking losses. A prediction model based on experimental data was used to predict the maize picking loss rate, and the rotational speed of the pulling rollers, operating speed, and header height were optimized to minimize the maize picking loss. The intelligent control system allows use of manual or automatic controls; the controller adjusts the rotational speed of pulling rollers, the operating speed, and header height based on the measured picking losses in automatic mode. The designed automatic control system comprises faster and slower loops. The fuzzy proportional–integral–derivative (PID) method is used to optimize the rotational speed of pulling rollers and the operating speed in the faster loop, and the PID method is used to regulate the header height in the slower loop. Field experiments were conducted to evaluate the effectiveness and stability of the system. The system test results showed that all working parts respond quickly, and the overshoot and steady-state errors of each working part were relatively small. Regardless of the load condition, the established control strategy could optimize each working parameter of the maize harvester. In experiments, maize picking loss rates of 1.676% and 1.386% were obtained, which meet the requirements of maize harvesting.