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.

To improve the uniformity and precision of soybean seeding, this study designed a high-speed airflow-assisted seeding system for the pneumatic drum-type high-speed precision seed-metering device. The system accelerates seed delivery through airflow and ensures precise seed placement using a seed press wheel. Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) coupling simulations were employed to analyze the seed motion trajectory, collision process, and velocity changes. Key design parameters of the airflow-assisted delivery system were optimized, including a tube diameter of 16 mm, a curved section radius of 80 mm, a seed delivery angle of 33.65°, and a press wheel diameter of 254 mm. The simulation results indicated that the relative position between the seed delivery tube and the seed drum significantly impacts seed trajectory and uniformity. Lowering the tube to align with the seed velocity direction minimized collisions and enhanced seed spacing consistency during high-speed operation. Increasing inlet air pressure improved seed distribution uniformity by accelerating seeds within the tube, reducing travel time and collisions; a 500 Pa pressure increase raised the maximum flow velocity by approximately 5 m/s. However, seed acceleration exhibited diminishing returns: pressure increase from 2.5 kPa to 3.5 kPa increased seed speed by 2.1 m/s, while a further increase to 4.5 kPa only added 1.1 m/s. The optimal inlet pressure for efficient energy transfer and seed acceleration was approximately 3.5 kPa. The press wheel played a crucial role by dispersing the impact force when seeds contact the soil, which achieved high capture rates above 94.0% across the seed drum rotary speed range of 11 to 19 rpm. This research provides theoretical and experimental support for the optimization of high-speed airflow-assisted seeding systems, offering significant practical value for large-scale agricultural production by enhancing seeding efficiency and quality.

Soil compaction is a pivotal factor influencing crop growth and yield, and its accurate assessment is imperative for precision agricultural management. Soil mechanical resistance is the key indicator of soil compaction, with accurate measurement enabling precise assessment. Dynamic soil mechanical resistance measurement outperforms conventional manual fixed-point sampling in data acquisition efficiency. In this paper, a methodology is proposed for the dynamic acquisition of soil mechanical resistance using a flexible thin-film pressure sensor. This study dynamically captures soil mechanical resistance at three depths (5 cm, 10 cm, and 15 cm) under dynamic machinery operating conditions. A device was designed for the detection of soil mechanical resistance, and a prediction model for soil mechanical resistance was developed based on the Kalman filter algorithm. Tests were conducted under steady-state and variable-load conditions, and the predicted values accurately tracked the reference pressure. Soil tank trials showed that at an operating speed of 0.69–0.72 km/h, the average prediction errors for the three soil layers were 2.03%, 1.48%, and 6.27%, with the coefficient of determination (R²) between predicted and measured values reaching 0.96. The system effectively predicts multi-depth soil resistance, providing novel theoretical and technical approaches for dynamic acquisition.

Soil treatment is one of the most energy-intensive agricultural processes. While power take-off (PTO)-powered rotary tillage tools are widely used due to their operational advantages, their energy efficiency requires enhancement. A new PTO-powered rotary tillage tool was designed, with cutting blades inclined at angle β to prevent soil mass accumulation due to soil sliding along the blades, thereby enhancing energy efficiency and tillage quality. A kinematic model was developed to analyze the tool’s motion trajectories. Theoretical analysis substantiated the optimal inclination angle β = 38–42° and elliptical-profile edge configuration of the cutting blades. During field experiments for performance evaluation, the angle of attack was in the range 20° < α < 40°, and the kinematic coefficient varied in the range 1.0 < η < 1.21 in 0.07 increments. Results demonstrated that draught force and torque reduced by 1.3–1.5 and 1.1–1.4 times, respectively, with an increasing kinematic coefficient. Minimal specific total power requirements of 4.5–4.7 kW/m were obtained at the optimal kinematic coefficient, η = 1.14–1.21, and angle of attack, α = 40°. Compared to base ring tillage discs, the new design reduces total power requirements by 14–16%. Furthermore, it provides required tillage quality: soil pulverization ≥ 80%, weed cutting ≥ 97%, crop residue retention ≥ 60%, and roughness of the field soil surface ≤ 3 cm.

Aiming at the problems of mutual overlapping in high-throughput seed flow and difficulty in accurate detection of seeding rate during high-speed precision wheat seeding, a double-layer orthogonal through-beam photoelectric detection device for high-throughput wheat seed flow was developed in this paper, based on a four-layer staggered hook-type precision wheat seed-metering device. Combined with the least squares method for threshold optimization and an error compensation model, the detection accuracy was effectively improved. Bench test results show that the detection accuracy of the device is stable above 97% at medium and low seeding frequencies of 20–40 Hz, which can meet the requirements of conventional operations. When the seeding frequency increases to 80–120 Hz, the accuracy decreases to 89.05% due to the increase in seed flow density. After introducing the compensation model, the accuracy remains above 95% in the high-frequency range of 90.2–140.2 Hz, which is nearly 10 percentage points higher than that without compensation. The research results can provide effective support and a technical approach for the accurate online detection of high-frequency seed flow in high-speed precision wheat seeding.

Arable land quality is of the essence for the sustenance of grain production and food security. The continuous monitoring of the physical and chemical properties of arable land is instrumental in facilitating a comprehensive understanding of the evolution patterns of soil quality. This, in turn, provides fundamental evidence that is crucial for the optimization of cultivation practices, the establishment of appropriate plough layers, and the enhancement of soil quality. The near-surface sensing methodologies facilitate the acquisition of soil data at reduced scales, thus signifying a pivotal research trajectory for the procurement of soil-related information. The present study undertakes an examination of the current state of research on acquiring key parameters of farmland soil and provides an overview of the fundamental ground-level techniques employed for the assessment of farmland soil parameters. These techniques encompass single-parameter fixed-point detection, encompassing Soil Moisture Content (SMC), Soil Electrical Conductivity (EC), and nutrient analysis, multi-parameter fusion detection, and dynamic parameter monitoring. The study systematically reviews field sensing methods for major soil physicochemical parameters (such as SMC, Soil Penetration Resistance (SPR), EC, and nutrients) while analyzing the current application of Artificial Intelligence (AI) in soil parameter detection. The present paper proposes a developmental trajectory that shifts from “single-parameter static” to “multi-parameter dynamic” monitoring. This trajectory is proposed as a building upon the analysis of existing research. This evolution emphasizes intelligent algorithm-driven data enhancement to improve detection accuracy, forming a closed-loop progression of “dynamic detection—precise modeling—decision support”. This framework provides a reference for the advancement of soil sensing monitoring technologies and the scaling of precision agriculture applications.