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.

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.

Agrivoltaics, an emerging approach that integrates solar energy generation with agricultural production, offers an effective solution to land-use conflicts by enabling the simultaneous production of clean energy and crops. However, the shading effect of photovoltaic (PV) modules significantly alters both the quantity and distribution of light within crop canopies, creating challenges in balancing power output with crop light requirements. This study employs the Rhino–Grasshopper parametric modeling platform, combined with Ladybug and PVsyst, to conduct batch simulations of 44 configuration schemes for an agrivoltaic system in Lianyungang, Jiangsu Province. Annual simulations of the light environment and energy generation were performed, and model accuracy was validated through field measurements using Daily Light Integral (DLI), light uniformity (coefficient of variation, CV), and annual energy yield as key indicators to assess the effects of different module layouts and tilt angles. The results reveal pronounced seasonal variations in the system’s light environment. The tilt angle exhibits a seasonal reversal pattern: higher tilt angles in winter and spring substantially reduce DLI (up to a 44% decrease under high ground coverage ratio, GCR, conditions), whereas moderate tilt angles in summer and autumn enhance light transmission, with low-GCR layouts enabling DLI values exceeding 30.6 mol·m⁻²·d⁻¹. Light uniformity was highest in the dual-row layout with 0.2 m spacing, maintaining a CV between 0.16 and 0.18. Energy yield peaked at a 30 tilt angle, showing a parabolic response pattern. Overall, this study proposes a photovoltaic module layout design method based on seasonal light distribution characteristics and the balance between agricultural and energy production goals. This method provides a scientific basis for structural layout planning and planting-model design in agrivoltaic systems and contributes to improving light-energy utilization efficiency and agricultural output, thereby achieving synergistic benefits between photovoltaic power generation and crop production.

To address the issues of low efficiency, high cost of manual harvesting, and the lack of mechanized harvesting technology and equipment for high-stem Chrysanthemum coronarium, a self-propelled orderly harvester was designed to perform key harvesting operations such as row alignment, clamping and cutting, orderly conveying, and collection. Based on the analysis of agronomic requirements for cultivation and mechanized harvesting needs, the overall structure and working principle of the machine were described. Meanwhile, the key components such as the reciprocating cutting mechanism and orderly conveying mechanism were structurally designed and theoretically analyzed. The main structural and operating parameters of the harvester were determined based on the geometric and kinematic conditions of high-stem Chrysanthemum coronarium during its movement along the conveying path, as well as the mechanical model of the conveying process. In addition, a three-factor, three-level Box-Behnken field experiment was also conducted with the experimental factors including the machine’s forward, cutting, and conveying speed, and evaluation indicators like harvesting loss rate and orderliness. A second-order polynomial regression model was established to analyze the relationship between the evaluation indicators and the factors using the Design-Expert 13 software, which revealed the influence patterns of the machine’s forward speed, reciprocating cutter cutting speed, conveying device speed, and their interaction influence on the evaluation indicators. Moreover, the optimal parameter combination, obtained by solving the optimization model for harvesting loss rate and orderliness, was forward speed of 260 mm/s, cutting speed of 250 mm/s, and conveying speed of 300 mm/s. Field test results showed that the average harvesting loss rate of the prototype was 4.45% and the orderliness was 92.57%, with a relative error of less than 5% compared to the predicted values. The key components of the harvester operated stably, and the machine was capable of performing cutting, orderly conveying, and collection in a single pass. All performance indicators met the mechanized orderly harvesting requirements of high-stem Chrysanthemum coronarium.