A bench test apparatus was developed to address the impact of varying terrain undulation on sowing depth in multi-row wheat sowing machines. In addition, a real-time sowing depth control model was proposed and implemented, enabling automatic adjustment of the sowing depth and ensuring uniform seed placement. The model operates by first specifying a target sowing depth, then acquiring real-time sowing depth measurements via a laser range sensor and terrain feature data ahead of the machine via an array-based LiDAR sensor. These two data streams undergo multi-sensor fusion to produce an accurate error and error rate. A fuzzy PID control algorithm then performs online parameter tuning of the PID gains, generating the control output needed to drive the stepper motor and adjust the depth-limiting wheel height, thereby precisely regulating the sowing depth. Experimental results demonstrate that under representative test conditions, the system achieves excellent sowing depth control performance; average error reductions of 10.7%, 22.9%, and 9.6% were observed when using fuzzy PID control versus no control. This work provides a technical foundation for intelligent sowing depth control in wheat sowing machines and lays the groundwork for future in-field adaptive operation and multi-scenario integrated control.

In order to meet the sowing demand of the hybrid rice plug seedling, a horizontal rotary precision seed metering device was proposed. The key structure parameters were designed, the kinematics analysis of the seed-filling process was carried out, and the main influencing factors of the seeding performance evaluation index were determined. Taking motor speed, the eccentric distance of the disk, and seed layer thickness as test factors, and the taking qualification rate, repetition rate, and miss-seeding rate as seeding performance test indexes, the quadratic regression orthogonal rotation combination test was carried out, and the relationship between the test factors and evaluation indexes was approached by a quadratic polynomial. On the basis of discussing the influence of the test factors on the performance index, the regression surrogate mathematical model between the main performance index and the influencing factors was established. The value of the evaluation index of correlation degree p between the model variables was less than 0.01. Based on the regression surrogate mathematical model, the response surface of the qualified rate of the seeding performance evaluation index was established, and the test data were analyzed by multiple regression analysis. The rapid prediction of qualified rates under the influence of test factors was realized, and the optimum test parameters were obtained as follows: motor speed 165 r/min, an eccentric distance of disk 22 mm, seed layer thickness 28 mm, qualification rate 98.55%, miss-seeding rate 1.45%, and repetition rate 0%. According to the best combination of test parameters, the performance evaluation index test of the prototype was carried out, and the qualification rate, miss-seeding rate, and repetition rate were 98.34%, 1.66%, and 0%. The experimental results were in good agreement with the theoretical analysis, indicating that the horizontal rotary precision seed metering device can meet the agronomic requirements of hybrid rice. The research results could provide some reference for the design of a precision seeding device for industrialized plug seedlings.

To address the issue of reduced sowing depth detection accuracy caused by varying soil topography during the operation of wheat row drills, an indoor bench test device suitable for wheat row drills was developed. The device integrates a laser sensor and an array sensor for terrain and sowing depth detection. The laser sensor provides the detected sowing depth values, while the array sensor captures different terrain features. The actual sowing depth values are obtained through the indoor experimental setup. The experiment includes three types of terrain: convex, concave, and flat. The terrain slope matrix is obtained using the array sensor, and terrain feature values are extracted. The laser sensor is then used to obtain the detected sowing depth, and the actual sowing depth is manually measured. PCA analysis is conducted to correlate terrain feature values with sowing depth deviations. Results indicate that under different terrain conditions, the slope mean and slope standard deviation are the main components affecting sowing depth deviations. Compared to using a single sensor, this system enables more accurate sowing depth measurement by analyzing terrain features. The device provides valuable data support for controlling sowing depth under varying terrain conditions in subsequent operations.

The intelligent control of precision planting can detect and regulate the operation quality of the planter in real time, which plays an important role in improving the operation quality of the planter and the yield of the corn. In this paper, the control system of a corn precision planter is designed to realize the operating quality monitoring and electric driving of the seed-metering device. The planting quality is calculated by the time interval between the neighboring falling seeds, instead of the plant spacing, to improve the operational efficiency of the system. At the same time, the forward speed of the planter is obtained by radar, which is used to accurately match the speed of the seed-metering device with the forward speed of the planter. The velocity error of the radar is analyzed, and the relevant relationship of the radar output frequency and forward speed is established. Comparative test results of this system and the JPS-12 test bench show that the detection performance of the system is reliable, and the maximum detection error of the quality parameters is less than 2.88%. Field experiments were carried out to verify the operational performance of the control system. Two speed sensors, radar and GPS, were chosen to study the effect of speed measuring on the performance of the control system. We found that speed measuring has a significant effect on planting performance. The qualified parameters of radar were significantly higher than those of GPS, at a forward speed of 6–12 km/h. The qualification feeding index (QFI) of radar was 0.51%, 0.67%, and 2.05% higher than that of GPS at speeds of 6, 8, 10, and 12 km/h. The precision index (PREC) of radar was 17.60%, 5.44%, 16.81%, and 17.30% lower than that of GPS. Therefore, the control system based on the radar speed measurement developed in this paper can significantly improve the operating quality of the planter.

Deep tillage is a conservation tillage method that aims to break the plow pan layer. It provides significant benefits, including enhanced root development, improved soil quality, and substantial increases in crop yields. The depth of tillage is a crucial factor in assessing the effectiveness of deep tillage operations. Accurate regulation of tillage depth in deep tillage equipment is vital for ensuring the high-quality and efficient execution of these practices. The distribution of mechanical resistance within the soil can effectively indicate the location of the plow pan layer and serves as the main reference for setting the tillage depth for machinery. This paper examined the current state of research on tillage depth control technology for deep tillage operations. It focused on three main technical areas: soil mechanical resistance detection, tillage depth measurement, and tillage depth regulation. The report discussed the working principles of various technologies and compared the existing methods. Additionally, the paper analyzed the challenges faced in the development of tillage depth control technology in China and offers recommendations for future advancements. It highlighted that leveraging information and digital technologies to determine the distribution of the soil plow pan layer, along with the integration of efficient and intelligent control technologies for precise tillage depth regulation, represented a key direction for the future development of deep tillage operations.

In view of issues affecting manual on-demand sowing of spinach, such as high working intensity, low seeding efficiency, and high cost, a mechanized precision seeding device for spinach seeds is proposed, based on a spoon-wheel including a combined cell with two intersecting surfaces. The key structure was designed, and motion analysis was conducted employing SolidWorks software (2022). Additionally, the seeding process was simulated using EDEM simulation software (2022). The number of spoons, the radius of the spoons, and the speed of the seed wheel were chosen as the influencing factors, while the qualified index, reseeding index, and missing index were utilized as the evaluation indexes. A response surface method test based on the Box–Behnken design was carried out, and the test results were analyzed to obtain the optimal parameter combination for the seed-metering device. The field test results showed that when the rotation speed was 18 rpm, the radius of each seed spoon was 2.5 mm, and the number of seed spoons was 50, the average qualified index was 90.89%, the reseeding index was 8.22%, and the missed seeding index was 0.89%. The test results satisfy the technical production requirements for spinach seed sowing.