Aiming to achieve orderly seeding of giant Juncao seed stems, enhance seeding efficiency and accuracy, and prevent damage to seed buds, a reciprocating roller precision seed dispenser for giant Juncao seed was designed. This device consisted of a reciprocating seed feeding mechanism, a roller seed dispensing mechanism, and seed guiding components. Based on the physical characteristics of giant Juncao seed stems in the Northwest region and the agronomic requirements for planting the stems, theoretical analyses were conducted on the processes of seed feeding, dispensing, and output of the device to determine the structures and parameters of key components. The working process of the seed dispenser was simulated by using the EDEM method to analyze the forces and motions acting on the stems, confirming that no damage occurred during the seed feeding phase. Using seed filling rate, clogging rate, and qualified planting distance as experimental indicators, a second-order regression orthogonal rotary combination simulation experiment was designed. The optimal structural parameter combination for the seed dispensing roller was determined to be: groove depth of 16 mm, groove width of 24 mm, and surface spacing of 7 mm. The optimal structural parameters for the seed output port were as follows: side plate inclination angle of 45°, opening width of 45 mm, and opening length of 30 mm. To determine the best operational parameters for the seed dispenser, the qualified planting distance rate and the variation coefficient of planting distance were used as experimental factors in a second-order regression orthogonal rotary combination test. The results showed that when the rotation speed of the seed dispensing roller was 11.57 r/min, the height from the seed discharge port to the ground was 191.56 mm, and the inclination angle was 35.77°, the qualified planting distance rate reached 96.4%, with a variation coefficient of 9.98%. The working performance of the seed dispenser met the agronomic requirements for giant reed planting.

Aiming to enhance the operational adaptability of transplanters in hilly and mountainous terrain, as well as improve the quality and stability of their performance on slopes, a leveling control system based on super-twisting sliding mode active disturbance rejection control (STSMC-ADRC) was proposed for a previously developed transplanter. This system enabled real-time lateral leveling control of the transplanter operating along contour lines. A mathematical model of the servo-electric cylinder control was established according to the overall structure and leveling control principle of the transplanter to describe and analyze the dynamic performance indicators of the system. Based on the mathematical model of the servo-electric cylinder, which provided the driving force for leveling the transplanter, a super-twisting sliding mode active disturbance rejection controller was designed. Lyapunov stability analysis was conducted for the designed controller in combination with the established control system. A co-simulation platform using Matlab/Simulink and ADAMS was constructed to compare the performance of the proposed STSMC-ADRC with classical PID control and active disturbance rejection control (ADRC) under static conditions. The results indicated that under static leveling conditions, the leveling time with ADRC was approximately 29.5% shorter than that with PID control, while the STSMC-ADRC strategy reduced the leveling time by approximately 46.3% compared with PID control. Finally, static single-side bridge tests and field dynamic tests were conducted. The dynamic test results demonstrated that the STSMC-ADRC control algorithm performed the best in experiments conducted at speeds of 0.2 m/s, 0.3 m/s, and 0.4 m/s, with root mean square errors of 0.77°, 1.94°, and 2.32°, respectively, outperforming both ADRC and PID control algorithms.

Most of the existing solanaceous vegetable grafting machines are semi-automatic models. Due to the significant differences in the growth morphology of seedlings, the automatic seedling picking process is difficult to achieve. Based on the working requirements of the fully automatic row grafting equipment for solanaceous vegetables, an automatic scion seedling picking device was designed. This device adopted flexible gripper hands, which had strong adaptability to the growth form of seedlings. It can automatically supply, clamp, cut and transfer the scion trays, achieving the automation of seedling extraction for grafting operations, thereby promoting the upgrading of the grafting machine from semi-automatic to fully automatic operation. In order to verify the working performance of the scion picking device, taking chili seedlings in the grafting period as the object, the success rate of picking as the test index, and the thickness of the elastic piece, the structure of the clamping hand, and the scion growth offset as factors, a three-factor and three-level orthogonal test for scion picking was carried out. The test results showed that the influence degrees of the three factors on the success rate of picking, from large to small, were as follows: gripper structure, growth offset, and elastic piece thickness. The optimal combination was gripper structureⅢ, growth offset of 0~14 mm, and elastic piece thickness of 0.3 mm. The scion picking test was conducted under the optimal combination conditions. A total of 150 scions from three tray seedlings were selected as the seedlings, after experimental verification, the success rate of picking was 98.7%, meeting the performance requirements of the scion picking operation of the grafting machine.

Aiming to improve the simulation accuracy of the discrete element model for tea garden soil-organic fertilizer mixture, focusing on the combination of physical experiments and simulation to calibrate the parameters of tea garden soil and organic fertilizer, by using the slope method, rolling friction test, and coefficient of restitution measurement, the physical and contact parameters of soil and organic fertilizer were obtained, and the mixing angle was measured based on the cylindrical lifting method. The Plackett-Burman experiment was used to screen for significant influencing parameters, and the optimization interval was determined by combining the steepest slope test. A quadratic regression model was established by using the Box-Behnken response surface method to obtain the optimal parameter combination: soil-organic fertilizer recovery coefficient of 0.35, organic fertilizer rolling friction factor of 0.115, and organic fertilizer steel recovery coefficient of 0.606. At the same time, the applicable range of moisture content for the model (5%~10%) was clarified through experimental analysis. The verification test showed that the simulated stacking angle was 24.49°, with a relative error of 1.65% compared with the actual value. A prototype of a tea garden tillage and fertilization machine was built, field rotary tillage and fertilization experiments were conducted, and rotary tillage and fertilization experiments were simulated. The relative errors of soil coverage rate in field experiments and simulation experiments at different rotary tillage speeds (150 r/min, 200 r/min, 250 r/min, and 300 r/min) were 6.21%, 4.52%, 7.13%, and 7.72%, respectively. The soil-rganic fertilizer distribution in simulation experiments was consistent with that in field experiments, verifying the accuracy of the soil-organic fertilizer mixture discrete element model and contact parameters.

Aiming to address the issues of resource waste and environmental pollution caused by uniform fertilization in traditional methods, as well as the inadequate adaptability of existing variable-rate fertilization systems' lag compensation approaches, a prescription map-based variable-rate fertilization system for rice and wheat featuring spatio-temporal coupling compensation function was designed. The system employed an STM32F407 chip and CAN bus architecture for the control unit and central processor. Integrated with ArcMap GIS platform, it proposed a digital prescription map generation method based on agronomic expert decisions. The developed human-machine interface software supported GPS message reception/parsing, prescription map loading, and high-efficiency retrieval. Innovatively, a spatio-temporally coupled calibration model for fertilizer application adjustment was established through dynamic outlet positioning modeling and lag time self-learning algorithm, enabling automatic field calibration and dynamic compensation of system response time. Bench tests demonstrated that the self-learning algorithm achieved over 90.5% accuracy across working speeds (4~8 km/h) and fertilization gradients (0~450 kg/hm², 0~750 kg/hm², 450~750 kg/hm²). Field trials showed that with spatio-temporal calibration enabled, the average fertilization accuracy reached 97.4% within 50-meter segments—an 8.0 percentage points improvement over non-calibrated status—while maintaining only 2.5 percentage points accuracy fluctuation at 8 km/h. This system provided a high-precision, low-cost intelligent solution for variable-rate fertilization in rice/wheat cultivation, offering technical support for China's national policy of reducing fertilizer use while increasing efficiency.

Aiming to address the key issues of low irrigation water use efficiency and backward management levels in grape cultivation in arid valley regions. It focused on collecting big data on the habitat and physiology of grape crops, integrating agronomic theoretical data, and an integrated decision-making system for water and fertilizer management was designed based on comprehensive multi-data coupling analysis. The system utilized standard Modbus protocol sensors for data collection and employed a LORA low-power wireless networking transmission module with freely settable timing. By integrating devices such as cloud boxes, gateways, and DTUs, the system established a soil and environmental Internet of things (IoT) monitoring and collection system, achieving continuous online monitoring and data collection of soil and the environment. The LORA wireless network transmission adopted the Modbus-RTU protocol for data transfer. The system incorporated a crop cultivation model built with an optimized fuzzy PID control algorithm based on the revised PM model. Through this model, the system can drive decision-making for irrigation and fertilization management, optimizing the agricultural production process. It achieved online control of integrated water and fertilizer irrigation equipment, reducing the need for manual operation. Experimental results showed that compared with traditional PID and fuzzy PID, the system's overshoot was reduced by 13.6 percentage points and 3.6 percentage points respectively, the system response rate was increased by 58.2% and 26.6% respectively; water usage was reduced by 25.0% and 13.5% respectively, and fertilizer usage was reduced by 30.0% and 17.1% respectively. This system aimed to promote the upgrade of integrated water and fertilizer technology, transforming the decision-making mechanism from “theory-oriented, experience-guided, and fixed-value regulation” to a benchmark based on big data analysis and judgment, with the expectation of providing technical support for the realization of real-time and precise regulation of integrated water and fertilizer management in grape plantations in arid valley regions.

Aiming to address the needs of modern agriculture in monitoring the overall crop growth status and developmental trends across various growth stages, and enable rapid, non-destructive crop assessment, a handheld crop normalized difference vegetation index (NDVI) sensor based on an active light source was developed. The sensor integrated narrow-band LEDs at 650 and 850 nm, along with Si PIN photodetectors, ensuring stable light output and precise signal acquisition. NDVI values were measured by using signal modulation and demodulation techniques across 60 wheat canopies subjected to four different fertilization levels. Experimental results demonstrated excellent light source stability, with standard deviations at both wavelengths maintained within 0.9 mV. In optical calibration experiments conducted at varying measurement heights, the results exhibited a strong linear relationship to the calibration parameters, with correlation coefficients (R²) exceeding 0.97. When the measurement height ranged from 20 cm to 40 cm, NDVI outputs remained largely stable, with root mean square errors (RMSE) of 0.025 and 0.015, respectively. Moreover, the sensor effectively mitigated the influence of ambient light fluctuations, ensuring consistent measurement results. A comparative analysis with a reference instrument demonstrated strong agreement in NDVI measurements, with R² of 0.79 and RMSE of just 0.031. These results confirmed that the developed handheld crop growth sensor delivered high reliability and accuracy in practical applications, providing robust technical support for crop growth monitoring and a sound scientific foundation for modern agricultural production management.

In order to improve the online yield measurement accuracy of grain combine harvester and suppress the interference of body tilting and mechanical vibration during the yield measurement process, a pressure-type grain yield measurement system based on tilting angle and vibration compensation strategy was developed. The system consisted of a pressure-type grain yield measurement device, a GNSS positioning module, a data processor and a host computer. Firstly, a mathematical model of grain yield monitoring was constructed based on the functional relationship between grain yield and sensor pressure, inclination, vibration and humidity, and a test rig for grain yield monitoring was built to optimize the structural parameters of the device through orthogonal test combined with response surface analysis. The effects of the number of pressure sensors, the mounting position of the vibration absorber and the inclination angle of the device on the measurement error were investigated, and the optimal parameter combinations were determined to be five pressure sensors, the 3rd reference position (175 mm from the left slot wall) for the vibration absorber and the 21° inclination angle of the device, respectively. The results of univariate (flow rate change) and multivariate (flow rate, inclination angle and vibration change) tests showed that the yield measurement error of 20 groups of tests under different working conditions was stably controlled within 3%; the results of field tests showed that the average yield measurement error in actual operation was 3.41%, with a maximum of no more than 4%, which verified that the system can effectively inhibit the interference of the tilting of the vehicle body and the vibration of the machinery on the accuracy of the measurement of the yield and provide an effective technical support to obtain the information of the distribution of the yield of the precision agriculture. The system can effectively suppress the interference of body tilt and mechanical vibration on yield measurement accuracy, and provide effective technical support for the acquisition of precise agricultural yield distribution information.

Aiming to address the issue that the threshing and separation device of traditional soybean combine harvesters cannot adjust the threshing gap in real-time, resulting in uneven material distribution in the threshing chamber and consequently high rates of soybean grain breakage, unthreshed grain loss, and entrainment loss, an adjustment system was designed based on the real-time matching of feed rate and threshing gap. Using the feed rate, threshing section gap, and separation section gap as experimental factors, and the grain breakage rate, unthreshed grain rate, and entrainment loss rate as evaluation indicators, bench tests, adaptive threshing gap adjustment tests, and comparative tests were conducted. The experimental results indicated that the average adjustment errors of the control system for the threshing and separation section gap were 0.49 mm and 0.47 mm, respectively. Under the test combinations where the feed rates were 0~2 kg/s, 2~3 kg/s, and 3~4 kg/s, with corresponding threshing section gaps of 15 mm, 23 mm and 30 mm, and separation section gaps of 15 mm, 20 mm and 27 mm, the grain breakage rate, unthreshed grain rate, and entrainment loss rate were 2.73%, 0.30% and 0.42%, respectively. Compared with the constant gap tests, these values were reduced by 0.39, 0.27 and 0.28 percentage points, respectively. All evaluation indicators from the adaptive threshing gap adjustment tests outperformed those from the comparative tests. The research result can support the development of adaptive threshing gap control systems and the intelligent of the soybean combine harvester.

Aiming at the saline peanut harvesting process in the traditional shovel pole type excavation and harvesting device exists in the soil crushing effect is poor, the root and pod part of the soil content rate is high, the excavation operation resistance is large and has other problems. Combining saline soil characteristics and peanut planting agronomic requirements, a cutting and squeezing combined excavation device was designed. A mechanical model for the cutting and squeezing forces of the digging device and a mechanical model for the fragmentation and separation of the peanut root-pod-soil aggregates were established. The main factors affecting the operational performance of the digging device were identified as the blade surface inclination angle of the digging shovel, the blade edge angle of the digging shovel, and the radius of the squeezing roller. The value ranges for these parameters were analyzed and determined. Simulation experiments were conducted by using EDEM software to investigate the dynamic changes in peanut root-pod-soil aggregates during the operation of the digging device. Single-factor experiments were used to determine the influence level of each factor on operational resistance and the soil adhesion rate on the root-pod system. Using operational resistance and soil adhesion rate as evaluation indicators, a quadratic regression orthogonal rotational composite experiment was carried out by employing the central composite design (CCD) method. The optimal operational parameter combination was determined as digging shovel blade surface inclination angle of 22°, digging shovel blade edge angle of 50°, and squeezing roller radius of 110 mm. Under these parameters, the soil adhesion rate on the root-pod system was 48.86%, and the machine operational resistance was 15 696.3 N. Field trial results demonstrated that the cutting-squeezing combined peanut digging device achieved a post-operation soil adhesion rate on the root-pod system of 53.61% and an operational resistance of 15 965.57 N. Compared with the original digging device, this represented a reduction of 21.58 percentage points in the soil adhesion rate on the harvested root-pod system and a reduction of 5.57% in operational resistance, indicating superior performance.