Accurately and effectively measuring the breaking quality of harvested corn kernels is a critical step in the intelligent development of corn harvesters. The detection of broken corn kernels is complicated during the harvesting process due to turbulent corn kernel movement, uneven lighting, and interference from numerous external factors. This paper develops a deep learning-based detection method in real time for broken corn kernels in response to these issues. The system uses an image acquisition device to continuously acquire high-quality corn kernel image data and cooperates with a deep learning model to realize the rapid and accurate recognition of broken corn kernels. First, we defined the range of broken corn kernels based on image characteristics captured by the acquisition device and prepared the corn kernel datasets. The corn kernels in the acquired image were densely distributed, and the highly similar features of broken and whole corn kernels brough challenges to the system for visual recognition. To address this problem, we propose an improved model called BCK-YOLOv7, which is based on YOLOv7. We fine-tuned the model’s positive sample matching strategy and added a transformer encoder block module and coordinate attention mechanism, among other strategies. Ablation experiments demonstrate that our approach improves the BCK-YOLOv7 model’s ability to learn effectively broken corn kernel features, even when high-density features are similar. The improved model achieved a precision rate of 96.9%, a recall rate of 97.5%, and a mAP of 99.1%, representing respective improvements of 3.7%, 4.3%, and 2.8% over the original YOLOv7 model. To optimize and deploy the BCK-YOLOv7 model to the edge device (NVIDIA Jetson Nano), TensorRT was utilized, resulting in an impressive inference speed of 33 FPS. Finally, the simulation system experiment for corn kernel broken rate detection was performed. The results demonstrate that the system’s mean absolute deviation is merely 0.35 percent compared to that of manual statistical results. The main contribution of this work is the fact that this is the first time that a set of deep learning model improvement strategies and methods are proposed to deal with the problem of rapid and accurate corn kernel detection under the conditions of high density and similar features.

To improve the expandability, seeding accuracy, and operating speed range of the electric drive system (EDS) of precision seeders, this study constructed an EDS based on a controller area network (CAN) bus and designed a motor controller based on a field-orientated control (FOC) algorithm. Full-factorial bench and field tests based on seed spacing (0.1, 0.2, and 0.3 m) and operating speed (3, 6, 9, 12, and 15 km/h) were carried out to evaluate the performance of the EDS. The results of bench tests showed that seeding quality varied inversely with operating speed and positively with seed spacing. The average quality of feed index (QFI) at 0.1, 0.2, and 0.3 m seed spacing in bench tests was 88.38%, 96.67%, and 97.36%, with the average coefficient of variation (CV) being 20.13%, 16.27%, and 13.20%. Analysis of variance confirmed that both operating speed and seed spacing had a significant effect on QFI and CV (p < 0.001). The analysis of motor rotational speed accuracy showed that the relative error of motor rotational speed above 410 rpm did not exceed 2.24%, and the relative error had less influence on the seeding quality. The average QFI was 85.93%, 95.91%, and 96.24%, with the average CV being 21.12%, 15.50%, and 16.49% at 0.1, 0.2, and 0.3 m seed spacing in field tests. The methods and results of this study can provide a reference for the design and optimization of the EDS in a maize precision seeder and provide an effective solution for the improvement of maize yields.

In order to solve the problem of high broken rate and impurity rate in the direct harvesting of corn kernels in China, a flexible threshing element with variable stiffness consisting of a conical spring and rasp bar was developed on the basis of the existing horizontal axial-flow flow corn threshing cylinder structure to achieve low loss harvesting of corn kernels. Through mechanical analysis of the key components of the threshing element, its structure and operating parameters were determined. Then, orthogonal tests were carried out using the feed amount, threshing clearance and cylinder speed as the test factors, and the broken rate and impurity rate of the corn kernels as the test indicators. The results showed that the feed amount, threshing clearance and cylinder speed had a significant effect on the broken rate and impurity rate of corn kernels; the optimum parameters for the corn variable stiffness flexible threshing device were feed amount 6.1 kg•s−1, threshing clearance 40 mm and cylinder speed 392 r•min−1. The broken rate of corn kernels was 1.67% and impurity rate 1.03%. The test results fully met the requirements of the national standards for corn harvesting operations. This study provides a technical basis for the application of the axial flow corn flexible threshing device in the corn direct harvesting combine.

A method to estimate modal frequency based on empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is proposed. This method can decrease the difficulties in identifying modal frequency of combine harvesters. First, we used 16 acceleration sensors installed at different test points to collect vibration signals of a corn combine harvester under operating conditions (mass time-varying conditions). Second, we calculated mean value, variance and root mean square (RMS) value of the vibration signals, and analyzed its stationarity of vibration signals. Third, the main frequencies of the 16 points were extracted using the EMD and EEMD methods. Finally, we considered modal frequencies identified by the SSI algorithm as standard, and calculated the fitting degrees of the EMD and EEMD methods. The results show that in different time periods (0~60 s and 60~120 s), the maximum differences of the mean value, variance and RMS value of signals were 0.8633, 171.1629 and 11.3767, and the vibration signal under the operating condition of field harvesting can be regarded as a typical non-stationary random vibration signal. The EMD method had more modal aliasing than EEMD, and when we obtained the fitting equations of EMD, EEMD and SSI methods, the value of the Euler distance between the EMD fitting equation and the SSI fitting equation was 446.7883, while that for EEMD and SSI was 417.2845. The vibration frequencies calculated by the EEMD method is closer to the modal frequencies identified by SSI algorithm. The proposed method provides a reference for modal frequency identification and vibration control in a complex working environment.

In order to solve low recognition of corn kernel breakage degree and corn kernel mildew degree during corn kernel harvesting, this paper proposes a real-time detection method for corn kernel breakage and mildew based on improved YOlOv5s, which is referred to as the CST-YOLOv5s model algorithm in this paper. The method continuously obtains images through the discrete uniform sampling device of corn kernels and generates whole corn kernels, breakage corn kernels, and mildew corn kernel dataset samples. We aimed at the problems of high similarity of some corn kernel features in the acquired images and the low precision of corn kernel breakage and mildew recognition. Firstly, the CBAM attention mechanism is added to the backbone network of YOLOv5s to finely allocate and process the feature information, highlighting the features of corn breakage and mildew. Secondly, the pyramid pooling structure SPPCPSC, which integrates cross-stage local networks, is adopted to replace the SPPF in YOLOv5s. SPP and CPSC technologies are used to extract and fuse features of different scales, improving the precision of object detection. Finally, the original prediction head is converted into a transformer prediction head to explore the prediction potential with a multi-head attention mechanism. The experimental results show that the CST-YOLOv5s model has a significant improvement in the detection of corn kernel breakage and mildew. Compared with the original YOLOv5s model, the average precision (AP) of corn kernel breakage and mildew recognition increased by 5.2% and 7.1%, respectively, and the mean average precision (mAP) of all kinds of corn kernel recognition is 96.1%, and the frame rate is 36.7 FPS. Compared with YOLOv4-tiny, YOLOv6n, YOLOv7, YOLOv8s, and YOLOv9-E detection model algorithms, the CST-YOLOv5s model has better overall performance in terms of detection accuracy and speed. This study can provide a reference for real-time detection of breakage and mildew kernels during the harvesting process of corn kernels.

The air distribution and speed uniformity of the cleaning fan in the cleaning room have a great influence on the working quality of the cleaning system of the harvester. In view of the problem of uneven air distribution in the cleaning room caused by improper adjustment of the main operating parameters of the cleaning fan in the cleaning device of the corn combine harvester, this paper takes the self-developed air screen cleaning test bench as the object. The main working parameters of the cleaning centrifugal fan (air supply distance, fan speed, and number of blades) were simulated and the Fluent simulation software was used to carry out the single-factor and multi-factor optimization tests, explore the influence law of each test factor on the air velocity in front of the screen, in the middle and behind the screen and the deviation degree of the airflow at the back of the screen surface, and find the optimal parameter combination. The data were systematically analyzed by multiple regression method and variance analysis method. The regression model of air velocity at the front, middle, and back of the screen and the air deviation degree at the back of the screen surface for the three working parameters of the cleaning fan were established. The optimal working parameter combination was obtained, that is, when the air supply distance is 580 mm, the fan speed is 1000 r/min, and the number of blades is 10, the airflow velocity in front of the screen is 10.8 m/s, the airflow velocity in the middle of the screen surface is 11.8 m/s, the airflow velocity at the back of the screen surface is 11.2 m/s, and the airflow deviation degree at the back of the screen surface is 13.5%. The relative errors were 1.9%, 0, 2.8%, and 3.0%, respectively. A combined test of the fan and the cleaning screen body with a feeding capacity of 8 kg/s was carried out, and the loss rate was 1.15% and the impurities rate was 1.24%. The regression model was reliable, and the optimal operation parameter combination performed well, meeting the technical requirements of cleaning operation, and providing theoretical guidance for the adjustment of fan structure and operation parameters in the cleaning system of the grain harvester.