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Based on the conventional meteorological observation data, the NCEP reanalysis data, the dual-polarization radar data, wind pro? filer radar data, and other datasets, the environmental conditions and dual-polarization radar characteristics of the phase transformation of precipitation in a rain and snow event in Hunan in late December 2021 is studied. The results are as follows: (1) The disappearing of dry lay? er, the thickening of wet layer, the whole-layer wet bulb temperature below 0 ℃, and the saturated stratification in the cold pad under the in? version layer before the snowfall favored the further cluster growth of falling snowflakes, resulting in the further enhancement of snowfall. (2) That the southwest jet exits at 700hPa and the strong water vapor convergences at 600-800 hPa, combined with the significant vertical up? draft at the middle and lower layers, provided sufficient moisture for the snowstorm. (3) The intermittent light rain (snow) in the evening of the 25th is closely related to the transit of short-wave trough frequently. In the morning of the 26th, the southwesterly wind turned to the westerly wind, which led to a short-term weakening of snowfall in Changsha. Furthermore, the warm-humid southwesterly flow strengthened over the cold pad, and the southeast wind in the cold pad turned to the northeast wind, which corresponded to the two peaks of snowfall in Changsha on the 26th. (4) There are certain differences in the correlation coefficient (CC), differential reflectance (Zdr), and horizontal reflectance factor (Zh) of different phases of precipitation. The dual-polarization products have a certain indication effect on the identification of rain and snow phases of this case. (5) The low-level"zero-velocity-level"is straight, and has the characteristics of wind-speed convergence, enhanced southwest jet core, and downward extension, etc., indicating that the dynamic climbing of warm-humid airflow over the cold pad would increase, and the snowfall could be maintained for a long time and would increase gradually. However, with the appearance of divergence of wind field, the snowfall gradually weakened. The findings of this study provide helpful criteria for the development, maintenance, and weak? ening of snow, which can be applied to the operational work of snowstorm nowcasting and forecasting.

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.

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.

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.