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Aiming at the problem of floating shellfish boxes interfering with kelp harvesting when mechanized kelp harvesting is based on shellfish–kelp mixed culture mode, this paper combines the structural characteristics of the shellfish box itself, designs the kelp harvesting unit test bench and develops a shellfish box separator device. The key factors affecting the box separator’s separation effect were derived through the theoretical analysis. The process of separation of a shellfish box by the box separator is simulated and optimized under the derived boundary conditions. The single-factor test for the separating effect of the box separator was conducted with ADAMS kinematics simulation software. The test showed the optimal utility intervals for the key factors under consideration. Further orthogonal tests were conducted for the three key factors, which were ranked in descending order of importance as box separator separation angle θ, box separator taper angle β and box separator placement depth h. The optimal parameter combination is the box separator separation angle of 31.9°, the box separator taper angle of 30° and the box separator placement depth of 550 mm. Verification experiments have shown that both indicators, the farthest horizontal distance of the shellfish box and the angle of the shellfish box deviating from the box separator, meet the actual production requirements. In summary, the separator can effectively separate the shellfish box from the kelp, and the device is simple in design, quick in operation, and accomplished separation without disturbing shellfish. This study can provide a theoretical basis for the separation technology of kelp and shellfish box under shellfish–kelp mixed culture mode.

The high-speed development of the liquor industry brings sorghum demand, which is increasingly strong at the moment. Still, its harvesting mechanization level is low, and with the design of a longitudinal flow sparse and dense curved-teeth sorghum threshing technology, the harvester’s work quality is improved by the reduction of seed impurities. This article describes the working principle of the harvester, the overall distribution of threshing elements, and force analysis of the threshing aspects to determine the structure of the threshing elements. The orthogonal test was carried out, with a sparse–dense interphase threshing drum as the research object, selecting operating speed, threshing element bending angle, and threshing element mounting angle as the test factors, with the entrainment loss rate and the net threshing rate as the assessment indexes for the three-factor, three-level test, and the use of Design-Expert to establish a mathematical regression model between the factors and the two indicators, resulting in the following optimized parameters: when the operating speed is 1.0 m·s−1, the bending angle of the threshing element is 80°, and the mounting angle of the threshing element is 45°, the loss rate of entrainment is 1.89%, and the net threshing rate is 95.53%. The machine’s design indexes are in line with relevant national standards and can meet the demand for mechanized harvesting of sorghum.

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.

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Aiming at the problems of uneven distribution of dislodged material on the screen surface of longitudinal axial flow grain combine harvester, a large difference in material clearing time, and large clearing loss, a dislodged material homogenizing device that can realize dislodged material return and homogenization at the rear of longitudinal axial flow was developed. (1) The structure and motion parameters of the reflux plate were determined, and simulation tests were carried out to verify them; (2) A test bench was set up, and the Box-Behnken test method was adopted to determine the influence law of each factor on the operating effect and the optimal parameter combination, and the results showed that the tilt angle of the return plate, motor speed, and amplitude had a significant influence on the distribution uniformity of the material on the screen surface; it was determined that the optimal combination of the angle of the return plate configuration was 28.7°, the speed of the motor was 247 r/min, the amplitude of the return plate was 18.3 mm, and the seed contamination rate was 0.48%. The optimum combination was determined to be 28.7°, 247 r/min, 18.3 mm, and 0.48% impurity rate; (3) under the conditions of the field test validation, the validation error is less than 5%, proving that it can effectively improve the performance of the clearing and reduce the rate of impurity content.

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 wind sieve cleaner is widely used in the screening system of combine harvesters due to its compact structure and efficient screening capability. In order to study more deeply the feeding load distribution of the combine harvester and the influence of the airflow field on the clearing effect, a mechanical analysis method was adopted to analyze the dynamics of the material in the inclined airflow, and a kinetic model was established. At the same time, the motion state of the material in the airflow field was explored, and combined with the actual orthogonal test, the response surface model of factors and indicators was established. Experimental validation was carried out. It provides an important research foundation and theoretical basis for optimizing the structural parameters of the screening system and improving its operational performance.

In order to solve the problem of the high damage rate of corn with a high moisture content in the Huanghuaihai region, according to the mechanical characteristics of corn in the Huanghuaihai region, we designed a flexible threshing device with a threshing rasp bar and variable stiffness spring as the buffer carrier, and the elements of the device were nail teeth and a flexible threshing rasp bar, which could be replaced freely. The experiment was based on the threshing rotor speed, threshing gap, and spring stiffness as the influencing factors, with the grain breakage rate and the unthreshed rate as an indicator of three-factor three-level experiments, and in October in Zibo through bench experiments, to determine the best parameter combinations. When the spring compression stiffness was 20.83 N/mm, the rotor speed was 375 r/min, and the gap of the concave plate was 45 mm, the grain breakage rate of the corn ear was 4.7%, and the unthreshed rate was 0.48%, which is in line with the relevant national standards.

In order to determine the distribution pattern of the threshed mixture in the double longitudinal axial flow threshing device, single-factor experiments were conducted on the self-developed experimental platform for the double longitudinal axis threshing device. The experimental factors included drum speed, threshing clearance, and feed rate. The variations in the distribution of the threshed material along the axial and radial directions were examined. The results indicate that the mixed material after threshing exhibits uneven distribution both axially and radially. Along the axial direction, the mass of corn kernels initially increases and then decreases and is predominantly distributed in the front one-third section of the drum. Meanwhile, the mass of corn cobs continuously increases. In the radial direction, the mass of corn kernels and cobs is higher in the middle and on both sides, with the corn kernels being most concentrated in the middle and the corn cobs mostly on the sides. Combining the corn kernel breakage rate and the unthreshed rate, the optimal operating conditions were determined as follows: a drum speed of 400 r/min, a concave clearance of 50 mm, and a feed rate of 16 kg/s.

In order to solve the problems of grain lifter in sorghum harvesting, such as ear loss and serious crop leakage loss, combined with the physical and mechanical characteristics of sorghum, the segmented and reverse spiral grain lifter for sorghum harvesting and cutting table was developed, and the design method of the main structural parameters of the grain lifter was determined. The comparative test of the working effect of the clasp showed that the working effect of the cutting table with the clasp was better than that without the clasp, which effectively reduced the harvest loss of the cutting table. By using Box–Behnken experimental design method, the influence law of forward speed, tilt angle, and rotation speed of grain lifter on the rate of ear loss and harvest loss in sorghum harvesting was investigated. The regression mathematical model and response surface of the rate of ear loss and harvest loss and analysis factors were established, and the optimal working parameters of the grain lifter were determined. The forward speed was 0.8 m/s, the tilt angle of the grain lifter was 28°, and the rotation speed of the grain lifter was 330 r/min. Under these conditions, the spike loss rate was 2.01, the leakage loss rate was 2.19, and the error with the theoretical value was less than 3%, which proved the rationality of the optimized combination parameters. In the harvest of crooked and fallen sorghum, the grain lifter can effectively reduce the loss of sorghum head drop and lodging leakage, ensure the reliability of the cutting table, and achieve low loss and efficient harvest of sorghum.

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.

In view of the large amount of wheat straw coverage and the difficulty with the high-speed working process used in traditional rotary tillage row cleaner. A row cleaner with staggered disc teeth for no-till planting was designed. It was determined that installation with a forward inclination α, horizontal declination β, and forward speed v0 of the machine were the main factors affecting the straw cleaning rate Y1 and working resistance Y2, and the range of values for structural parameters and motion parameters of the row cleaner were determined. Taking α, β, and v0 as the factors and Y1 and Y2 as the response indexes, using EDEM 2018 software to simulate the straw cleaning process under different parameters and determine the influence of each parameter on the straw cleaning performance. After performing a soil bin test, the results showed that there was no straw entanglement and blockage, and the passability was better than that of the traditional flat disc separated row cleaner. When α was 70°, β was 30°, v0 was 8 km/h, and the embedded depth h of the soil (straw) was 55 mm, the average straw cleaning rate was the highest, which was 90.59%. This study provides a new idea for the design of high-speed corn no-till planters in the Huang-Huai-Hai area of China.

Based on the mechanical test (shear test, compression test), the bond model of corn kernel and straw was established to explore the rolling and crushing effect of different crushing rollers. The type of crushing roller is different. The material crushing process by the force (extrusion and kneading) is different. The mechanical analysis of the crushing process reveals that the disc crushing roller (DCR) has the characteristics of large unit-length kneading area; the spiral-notched serrated crushing roller (SNSCR) has transverse shearing effect on the material; and they affect the crushing effect of the material. By means of discrete element method and simulation test, multiple regression method and variance analysis method are used to systematically analyze the data. The optimal working parameters of each roll (crushing roll speed, crushing clearance, differential ratio) were obtained. The simulation test and bench test of the crushing process of materials with different roll shapes were carried out under the optimal working parameters. The crushing effect was evaluated with a Binzhou screen and a corn silage grain-crushing score screen. The crushed materials of corn kernel can be divided into three categories according to the size (broken grains passed through 2 mm sieve; broken grains passed through 4.75 mm sieve; and broken grains that cannot pass through 4.75 mm sieve), and the crushed materials of corn stalk can be divided into four categories according to the size and thickness (broken straw through 4 mm sieve; broken straw through 8 mm sieve; broken straw through 19 mm sieve; and broken straw that cannot pass 19 mm sieve). The crushing effect and crushing classification of the simulation test and bench test were basically consistent. The results showed that the disc crushing roller group had the highest comprehensive score with straw rolling rate of 89.1% and grain crushing rate of 87.7%, which was the most suitable for harvesting whole-plant silage maize (WSM).

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.

In field harvesting conditions, the non-stationary random vibration characteristics of the harvester are rarely considered, and the results of vibration frequency calculated by different time–frequency transformation methods are different. In this paper, the harvester’s vibration characteristics under the time-varying mass were studied, and the correlation between vibration frequency and modal frequency was analyzed. Firstly, under the conditions of time-varying mass (field harvesting conditions) and non-time-varying mass (empty running condition), the non-stationarity characteristics of vibration signals at 16 measurement points of a combined corn harvester frame were studied. Then, fast Fourier transform (FFT), short-time Fourier transform (STFT), and continuous wavelet transform (CWT) were used to calculate the vibration frequency distribution characteristics of the corn harvester. Finally, based on the EFDD (enhanced frequency domain decomposition) algorithm, the correlation between the primary vibration frequency and the operating mode frequency is studied. The results show that the mean, variance, and maximum difference of the vibration amplitude under harvesting conditions (mass time-varying system) are 0.10, 26.5, and 1.0, respectively, at different harvesting periods (0~10 s, 10~20 s, 20~30 s). The harvesting conditions’ vibration signals conform to the characteristics of non-stationary randomness. The FFT algorithm is used to obtain more dense vibration frequencies, while the frequencies based on STFT and CWT algorithms are sparse. The correlation between the FFT method and the EFDD algorithm is 0.98, and the correlation between the STFT, CWT, and the EFDD algorithm is 0.99 and 0.98. Therefore, the primary frequency of the STFT methods is closer to the modal frequency. Our research laid the foundation for further study and application of mass time-varying combined harvester system non-stationary random vibration modal frequency identification and vibration control.

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.