A seed-metering device is confined to the seed-airflow-mechanical multi-field coupling mechanism during high-speed operation of the maize delta-row dense planting planter. It is also unclear on the seeds movement and airflow distribution, leading to the low performance of the device. This study aims to explore the overall structure and working principle of the air-pressure high-speed precision seed-metering device. Theoretical analysis, simulation, bench testing, and field trials were also carried out to investigate the influence of the key parameters on the performance of the device. Theoretical models were then established for the seed-filling and seed-cleaning of the device. DEM-CFD coupling simulation was used to explore the flow field of the device chamber and the shape holes. A systematic analysis was made on the variation trend of the average drag force and coefficient of variation of speed of the qualified delta-row group seeds in the seed-cleaning zone at different seed-cleaning angles. The specific motion was observed for the big-rounded, big-flat, small-rounded, and small-flat seeds in the device. Some indicators were defined for the quality evaluation on the delta-row sowing of the device, including the qualified index of delta-row, qualified index of row spacing, projection spacing qualifying coefficient of variation, and row spacing qualifying coefficient of variation. Full-factor bench tests were conducted with the seed-cleaning angle, chamber inlet pressure, and operating speed as the experimental factors. While the high-speed sowing field tests were conducted on the maize delta-row dense planting in different tillage patterns. Simulation tests show that the coefficients of variation of pressure and flow velocity were less than 2.9% and 4.8%, respectively, when the chamber inlet pressure was in the range of 3.6-4.2 kPa. There was a more uniform and stable distribution of the flow field in the device. The average drag force of the seeds in the qualified delta-row group was higher than 0.06 N in the range of the seed-cleaning angle of 3.0°-4.0°, where the coefficient of variation of speed was lower than 5.29%, indicating more stable seed-cleaning. In the motion of each type of seed, the average drag force of the big-rounded seeds was 0.0793 N from the stable seed-filling to the seed-unloading point, indicating the more suitable for the seed type of sowing. The bench tests showed that the qualified index of the delta-row of the device was more than 70%, and the projection spacing qualifying coefficient of variation was less than 12% at the seed-cleaning angle of 3.0°-4.0°, chamber inlet pressure of 3.6-4.2 kPa, and operating speed of 12-16 km/h. Field test results show that the qualified indexes of the delta-row of the device and row spacing were more than 72% and 85%, respectively. The projection spacing qualifying coefficient of variation was less than 14%, with the seed-cleaning angle of 4°, the chamber inlet pressure of 4.2 kPa, and the operating speed of 12-16 km/h. The row spacing qualifying coefficient of variation was less than 8% under various tillage patterns. The device can be expected to maintain the high quality of delta-row sowing in both flat breaking and ridge plowing modes, fully meeting the requirements for the precision sowing of maize. This finding can provide a basis to further improve the performance of the device.

Seeding is an important part of improving corn yield. Currently, seed guide tubes are mostly used as transport devices. But the existing seed guide tubes cannot meet the requirements or achieve the goal of fixing the seed falling trajectory. A seed collision phenomenon occurs occasionally. So, in response to the problems that the seeds and seed guide tube collide or bounce under high speed operation, which results in a lower sowing qualification rate and poor spacing uniformity, a seed receiving and conveying system comprising a belt-type high-speed corn seed guiding device was designed and optimized, to meet the needs of high-speed precision sowing operations and improve the spacing uniformity.The factors affecting the seed conveying performance were obtained by analyzing the mechanical properties of the seeds at various movement stages. These factors were the number of seed cavities between adjacent seeds, the forward speed, the height from the ground, and the installation angle. Single factor simulation experiments were conducted by selecting the paddle spacing as the test factor and using the pass rate, reseeding rate, omission rate and coefficient of variation as the evaluation indexes to investigate the influence of the paddle spacing on the seed guide performance of the device and further determine the structural parameters of the paddle belt. Orthogonal rotation combination tests of three factors and five levels were also conducted through bench testing.Then the test outcomes were optimized. The results indicated that the best results were obtained when the number of seed cavity intervals between adjacent seeds was 5.16, the installation angle was 79.40°, and the height from the ground was 31.84 mm. At this time, the qualified rate was 98.49%, the repeated sowing rate was 0.48%, the missed sowing rate was 1.03%, and the coefficient of variation was 6.80%. Experiments were used to validate the optimization results, and all of the obtained index data satisfied the criteria for accurate and quick corn sowing. The study’s findings can serve as a theoretical foundation for a belt-type high-speed corn seed guiding device optimization test.

In maize breeding, limitations on sampling quantity and associated costs for measuring maize grain moisture during filling are imposed by factors like the planting area of new varieties, maize plant density, effective experimental spikes, and other conditions. However, the conventional method of detecting moisture content in maize grains is slow, damages seeds, and necessitates many sample sets, particularly for high moisture content determination. Thus, a strong demand exists for a non-destructive quantitative analysis model of maize moisture content using a small sample set during grain filling. The Bayes-Merged-Bootstrap (BMB) sample optimization method, which built upon the Bayes-Bootstrap sampling method and the concept of merging, was proposed. A critical concern in dealing with small samples is the relationship between data distribution, minimum sample value, and sample size, which has been thoroughly analyzed. Compared to the Bayes-Bootstrap sample selection method, the BMB method offers distinct advantages in the optimized selection of small samples for non-destructive detection. The quantitative analysis model for maize grain moisture content was established based on the support vector machine regression. Results demonstrate that when the optimal resampling size is 1000 times or more than the original sample size using the BMB method, the model exhibits strong predictive capabilities, with a determination coefficient (R²)>0.989 and a relative prediction determination (RPD)>2.47. The results of the 3 varieties experiment demonstrate the generality of the model. Therefore, it can be applied effectively in practical maize breeding and determining grain moisture content during maize machine harvesting.