In Huang-Huai-Hai plain of China, farmers collect the maize straw for livestock during maize harvest to increase their revenue. To maintain the sustainable productivity of the soil, all straw after the wheat harvest is returned to the field. This straw brings difficulties in the no-till seeding for maize after wheat harvest, and thus it is necessary to develop efficient no-till seeders that can cope with heavy residue and improve sowing quality. In this work, we designed a wide-strip-till no-till pneumatic maize (WNPM) seeder to satisfy the need in this plain. The key parameters of the opposite-placed anti-blocking mechanism of the WNPM seeder were determined via the discrete element method (DEM) technology, while the parameters of the pneumatic maize seed meter were specified using the coupled simulation of computational fluid dynamics (CFD) and DEM. We also carried out field experiment to test the performance of our machine. Under the operating speed of 8 km/h, the soil disturbance was 38.2%. Moreover, the straw cleaning rate achieved 94.4% in the seeding belt while the residue cover index of the seed plot was over 58%, and the seeding performance was improved significantly. The qualified seed spacing index, uniformity variation coefficient, qualified index of sowing depth and variation coefficient of sowing depth were 96.6%, 19.1%, 95.1% and 3.2%, respectively. In general, the WNPM seeder improves the working efficiency of maize sowing because both the reliable working speed and the sowing quality were increased. These results are of considerable importance for crop production in Huang-Huai-Hai plain of China.

To tackle the challenges posed by substantial variations in target scale, intricate background interference, and the likelihood of missing small targets in multi-temporal UAV maize tassel imagery, an optimized lightweight detection model derived from YOLOv11 is introduced, named OTB-YOLO. Here, “OTB” is an acronym derived from the initials of the model’s core improved modules: Omni-dimensional dynamic convolution (ODConv), Triplet Attention, and Bi-directional Feature Pyramid Network (BiFPN). This model integrates the PaddlePaddle open-source maize tassel recognition benchmark dataset with the public Multi-Temporal Drone Corn Dataset (MTDC). Traditional convolutional layers are substituted with omni-dimensional dynamic convolution (ODConv) to mitigate computational redundancy. A triplet attention module is incorporated to refine feature extraction within the backbone network, while a bidirectional feature pyramid network (BiFPN) is engineered to enhance accuracy via multi-level feature pyramids and bidirectional information flow. Empirical analysis demonstrates that the enhanced model achieves a precision of 95.6%, recall of 92.1%, and mAP@0.5 of 96.6%, marking improvements of 3.2%, 2.5%, and 3.1%, respectively, over the baseline model. Concurrently, the model’s computational complexity is reduced to 6.0 GFLOPs, rendering it appropriate for deployment on UAV edge computing platforms.

To solve the problems of low seeding precision and the poor operational adaptability of traditional no-till seeders under dense planting mode, and meet the agronomic requirements for high maize yield, this study carried out optimization and experimental research on the no-till precision fertilizer-seed co-sowing system for maize with wide-narrow row dense planting, relying on the experimental base of the Science and Technology Courtyard for Super High-Yield Cropping Systems in Qihe, China Agricultural University. Through modular integration and the optimization of key components, precise row spacing adjustment and improved sowing depth consistency in complex plots were achieved. A tractor-implement integrated a kinematic model and a dynamic model of the seed metering tube, which were constructed to quantify the correlation between operational parameters and motion states, providing theoretical support for structural parameter optimization. Field tests showed that all operational quality indicators of the system met the local high-yield requirements for no-till dense planting; the comprehensive performance was optimal at a density of 75,000 plants·ha⁻¹, with the best seeding uniformity (coefficient of variation: 5.65%), seedling emergence and seedling uniformity, which is well adapted to the agronomic characteristics of the wheat–maize rotation areas in the Huang-Huai-Hai Plain. Subsequent optimization by reducing the operating speed and increasing the spring stiffness can further improve the operational quality, realize the deep integration of agronomy and agricultural machinery, provide agricultural machinery support for high-yield and high-quality maize cultivation, and is of great significance for improving agricultural production efficiency and resource utilization.

Pneumatic wheat seed-metering device often has a poor seed-filling state under the high-speed working condition, which adversely affects precision seeding. This paper describes a wheat seed-metering device that employs a combined mechanical-pneumatic seed-filling method. The orifice structure and seed-filling space at the nozzle are optimized to improve the seed-filling efficiency and seed-carrying stability. The working process of the wheat seed-metering device was analyzed, the principles of the seed-filling and seed-cleaning processes were elucidated, the factors influencing the seed-filling performance and seed-carrying stability were investigated, and the discrete element method-computational fluid dynamics (DEM-CFD) simulation method was employed to simulate the working process. The nozzle-orifice diameter, seed-filling-space angle, and rotation speed were determined as the simulation influence factors, and the trends of the nozzle void fraction, resistance to clearing, and initial dropping position were analyzed from a microscopic scale. On a macroscopic scale, the seedleakage rate, reseeding rate, and grain-spacing qualification rate as performance-evaluation metrics were analyzed, and the significant order of impacts for these influencing factors and their interaction effects was obtained. Through secondary optimization experiments, the optimal parameter combination was determined to be a nozzle-orifice diameter of 1.7 mm, a seedfilling-space angle of 39.3°, and a rotation speed of 657.4 r/min. Static bench-validation tests and indoor vibration-environment simulation experiments were conducted, and the results were compared with a control group. The findings reveal that when the seed-metering plate rotation speed varies between 400 r/min and 1000 r/min, the optimized seed-metering plate exhibits a seedleakage rate below 7.6% and superior performance in all indicators compared to the original seed-metering plate, indicating a significant improvement in seed-filling efficiency and seed-carrying stability.

Aiming at the problems of excessive soil disturbance caused by deep plowing and stripped straw backfilling in strip tillage machinery, which are induced by the large amount of residual straw before maize sowing in the Huang-Huai-Hai Region, an integrated tillage machine suitable for pre-sowing strip tillage of summer maize—integrating subsoiling, stubble-crushing, and soil-guiding functions—was designed. First, the physical properties of straw were analyzed to determine the tooth profile parameters of the stubble-crushing wheel. The unique convex structure of the tooth disc enables it to simultaneously perform depth-limiting and soil-pressing functions. By calculating the flow characteristics of soil during tillage, the angle and distance between the subsoiling shovel and the stubble-crushing wheel were designed. This not only enhances soil crushing and flow but also reduces the occurrence of blockages. A discrete element simulation test with quadratic orthogonal rotation combination was conducted. The machine’s forward speed, wheel position distance, and wheel deflection angle were selected as test factors to analyze their effects on the soil loss rate of the seedbed strip and straw backflow effect under different combinations. The optimal combination of parameters was determined as follows: forward speed of 7.383 km/h, front–rear position distance parameter of −10.131 cm, and deflection angle of 8.608°, with the soil loss rate of the seedling belt reaching 5.486% under this condition. Field experiments were conducted in combination with the strip tiller to verify the simulation-optimized parameters, and comparative experiments at different speeds were also carried out. The field experimental results showed that the deviation of the actual soil disturbance rate caused by the machine from the simulated value was −1.166%, and the soil disturbance rate within the seedling belt was even lower. The results indicated that after the operation of the improved machine, there were no obvious ruts on the soil surface, and the straw was evenly distributed at the edge of the seedling belt, which meets the agronomic requirements for maize planting.

Professors Gao Wangsheng and Chen Yuanquan confirmed that they only provided general guidance and suggestions in the early stages of the research, and did not participate in substantive work such as experimental implementation, data analysis, and manuscript writing