Aiming to address the challenges of high labor intensity, low operational efficiency, severe tobacco leaf damage, and lack of suitable harvesting equipment in tobacco-growing areas such as Yunnan, Guizhou, and Hunan provinces, an optimized chassis for a tracked self-propelled semi-automatic tobacco harvester was designed, specifically for hilly regions. The machine featured a gantry frame that enabled simultaneous harvesting three rows of tobacco. To reduce mechanical damage to the tobacco leaves and meet the needs of stable operation at low-speed, a dual-motor independent drive system and a mechanical automatic row-following device were designed. Additionally, based on CAN bus technology, an integrated vehicle control system combing manual and remote control was developed to meet control needs in various scenarios. Stability analysis and performance testing of the machine were carried out. The results showed that the maximum speed of the harvester chassis was 5 km/h with minimum turning radius of 903 mm. And it could handle slopes with up to 23°, and the lateral ultimate tipping angle was 24.7°. There was no side-slip or rollover when operating on contour lines with a lateral tilt angle of 15°~20° on sloping land. Moreover, it could stop for more than 5 minutes on both upward and downward directions along longitudinal slopes ranging from 10° to 15° without any issues. During field harvesting, the machine exhibited smooth running characteristics with ample power reserves allowing free speed adjustments between 0 km/h and 4.5 km/h. This highlighted its strong adaptability to manual tobacco leaf harvesting operations. The average mechanical damage rate to tobacco leaves was 0.73 leaves per operation per hectare, and the average mechanical damage rate to tobacco plants was only 0.083 plants per operation per hectare. It showed that the developed tracked self-propelled semi-automatic tobacco leaf harvester chassis could meet the requirements of manual and machine-operated harvesting in narrow fields and gentle slopes, which was of significant importance for promoting the full mechanization of tobacco production.

In order to solve the problems of low adaptability of the tool to the banana crown and the low success rate of the banana in the process of intelligent de-handing, a type of end effector was designed according to the morphological parameters of the banana bunch and the crown and the production requirements of the de-handing. The cutting arc of the adjustable opening-closing de-handing tool was used to adapt to different sizes of the banana crown to achieve continuous banana de-handing. The key parameters and main structure of the de-handing end effector were designed. The mechanical analysis model of the process of cutting banana crown by the de-handing tool was established, and the strength simulation was carried out. The results showed that the maximum stress of the de-handing tool during the de-handing process was 58.147 MPa, which was less than the yield strength of the tool material 205 MPa. The test platform of robot de-handing was built, and the significant factors affecting the de-handing effect were sorted as the cutting angle of de-handing, the blade angle and the de-handing speed. In order to obtain the optimal parameter combination, taking the peak cutting force and incision quality as the evaluation indices, the combination test of three-factors and three-levels were carried out by using Design-Expert software, and the mathematical regression model between the experimental factors and the evaluation indexes was obtained. The multi-objective optimization of the regression model was carried out, and the optimal parameter combination was obtained as follows: the de-handing speed was 2 m/s, the cutting angle of de-handing was 5.77°, and the blade angle was 5°. The results of the optimal parameter combination verification test showed that the peak cutting force was 31.49 N, the incision quality was 84 points, the relative error with the prediction results of the regression model was less than 5%, and the success rate of the de-handing was 92%, indicating that the opening-closing end effector of banana de-handing can effectively adapt to the banana crown's morphology and obtain better incision quality.

In recent years, film mulching technology has been widely used in agriculture. After cotton harvest, large amounts of residual film need to be recycled, simultaneously, cotton stalks crushing needs to be carried out, in Xinjiang, China. The existing integrated machine for straw crushing and residual film recycling in China can be mainly divided into drum-type and chain teeth type. Considering the current drum-type integrated machine, which has a low residual film recovery rate, and high impurity content in the residual film, the rubbing-type residual film baling mechanism was prone to throw out the residual film and was easy to deform the baling belt and other problems, an integrated machine for straw crushing and residual film recycling and baling was developed. Through principle analysis and calculation, the key parameters of the hammer blade straw crushing mechanism and the screw conveying mechanism were determined; the residual film picking mechanism and the wheel drive mechanism were improved, and the dimensions and rotation speed of the film pick-up drum, the transmission ratio between the wheel and the film pick-up drum, the dimensions of the film pick-up teeth and the space between adjacent film pick-up teeth were determined; through theoretical analysis, the structural parameters and operating parameters of the screw impurity removal mechanism and the film removal mechanism were determined; by analyzing the process of residual film baling, the parameters such as the angle between the baling belt and the horizontal plane, the linear speed of the baling belt, and the number and space of the support rollers were determined. The field test demonstrated that the optimal operating parameters of the machine were as follows: the machine's advancing velocity was 10 km/h, the depth of the film-picking teeth was 25 mm, and the height of the stubble was 10 cm. Validation test results demonstrated that under the aforementioned optimal operating parameters, the film pickup rate was 89.78%, the soil content was 11.15%, and the pass rate of crushed straw was 94.79%.

Aiming to address the issues of missing accuracy evaluation for tractor dynamic models and the lack of systematic, standardized assessment methods, an accuracy evaluation approach was proposed based on time-response error and grey relational analysis, grounded in the verification, validation, and accreditation (VV&A) theory of model simulation systems. A hierarchical validation index system comprising phase error, amplitude error, and shape error was constructed, and the grey relational degree method was applied for segmental evaluation of dynamic response curves. Four sets of idealized test cases with defined error components were designed and analyzed by using both the proposed method and traditional numerical error analysis techniques. The results demonstrated that the proposed method effectively decoupled and accurately identified multiple error characteristics in each test case, precisely reflecting the sources and distribution of errors, while its comprehensive evaluation accuracy outperformed traditional methods. Furthermore, taking a self-developed longitudinal dynamic model of a power-shift tractor as an application case, real vehicle data were collected under five road conditions: tilled field, untilled field, stubble field, concrete surface, and mixed road surface. The proposed method was used to compare and analyze simulation results against real vehicle data under various working conditions. The results indicated that the mean comprehensive accuracy score of the model was 90.95. Under high-speed and complex road conditions, scores for various error characteristics decreased significantly, with comprehensive results in some extreme conditions falling below 88, reflecting certain limitations of the model under different working conditions, which aligned with objective reality and verified the effectiveness and comprehensiveness of the proposed evaluation method.

In order to solve the problem of lack of accurate modeling and parameter support for multi-component heterogeneous particles in the clearing link during the combined millet harvesting process, a research on the modeling and parameter calibration of grain threshing mixture was carried out. Based on the compositional characteristics of millet threshing mixture, three types of typical particle models were constructed for seeds, millet panicle clusters and stalks, and the discrete element method combined with the auto-filling technology was used to model spheres, heteromorphs and cylinders, respectively, and the modeling morphology was optimized through the analysis of the number of filled spherical surfaces and the degree of morphological differences. Based on the determination of physical parameters of particles, using the angle of repose as an index, the parameter calibration method combining Plackett-Burman test, the steepest ascent test and Box-Behnken response surface optimization was adopted to realize the correction of contact parameters of seeds, millet panicle clusters and short stalks, and the average errors of simulation and actual angle of repose were 1.94%, 3.72% and 1.16%, respectively. In order to verify the accuracy of the model, high-speed camera validation tests were carried out, and the particle trajectories were extracted by using TEMA software and compared with the simulation results. The results showed that the overlap of the three types of particle trajectories was greater than 0.75, and the obtained mean absolute error and root mean square error were at a low level, indicating that the simulation trajectories were highly consistent with the measured results in terms of amplitude and trend, which verified the accuracy, and the model constructed was highly consistent with the measured results. The results showed that the modeling method and calibration parameters constructed had good physical consistency and engineering applicability, which can provide effective support for the particle dynamics simulation and mechanism research of millet cleaning process.

Aiming to address issues with traditional mechanical adjustment methods for no-till precision planters in stubble fields, such as the ground wheel assembly's inability to adapt to terrain changes, leading to unstable residue clearing performance and increased wheel slip, a hydraulically controlled terrain-following depth-limiting ground wheel assembly was designed. Its hydraulic system enabled rapid adjustment of wheel height while maintaining a constant position, ensuring stable furrow opener depth. The cushioning spring reduced ground-induced impacts transmitted to the planter, enhancing the stability of the residue clearing device. Through theoretical analysis, the main structural parameters of the terrain-following depth-limiting ground wheel assembly were determined. Simulation tests identified the optimal stiffness of the cushioning spring as 1.2×10⁵N/m. Comparative field trials demonstrated that the hydraulically controlled terrain-following depth-limiting ground wheel assembly operated stably with lower slip rates. When the furrow opener depth qualification criterion was set at (50±10) mm, the qualification rates for opener soil penetration depth were 62% and 82% for the mechanically-adjusted ground wheelassemblyandhydraulic-controlledterrain-followingdepth-controlwheelassembly, respectively. The hydraulic assembly exhibited a 20 percentage point higher qualification rate than the mechanical assembly. Furthermore, the hydraulic terrain-following wheel demonstrated significantly lower slip rates. While both adjustment methods showed increasing slip rates with the rise of operating speeds, the hydraulic terrain-following wheel exhibited a smaller rate of increase.

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.

Near-infrared spectroscopy has been widely used for the rapid analysis of the physical and chemical properties of straw, offering advantages such as fast detection speed, high efficiency, and non-destructive testing. However, in practical applications, differences in straw particle size can reduce the detection accuracy and stability of the model. For rice straw samples with varying geometric particle sizes, near-infrared spectra were obtained in the 10 000~4 000 cm^-1 wavelength range. Polynomial fitting (PF) was employed to remove background signals from the spectral data of the straw samples. A one-way analysis of variance (ANOVA) was used to perform statistical testing (P<0.05) on the spectral feature signals of the geometrically sized straw samples after polynomial fitting. Principal component analysis (PCA) and Biplot analysis were employed to assess the contribution of spectral feature signals to the spectral differences among straw samples with different geometric particle sizes. Pearson and Spearman correlation analyses were used to screen for key spectral feature signals. Based on the quadratic polynomial fitting mathematical model, it was assumed that absorbance was composed of particle size-related spectral information (PSRSI) and non-particle size-related spectral information (nPSRSI). A particle size regression correction method (PSRCM) was proposed based on the absorbance and particle size of key spectral feature signals. The results indicated that the spectral feature signals at 5 180 cm^-1 of the geometric particle size straw samples exhibited extremely significant differences (P≤0.05, P≤0.01, P≤0.001) and showed a highly significant correlation with particle size (Pearson's r was 0.96). The quadratic polynomial mathematical model performed optimally, with an average coefficient of determination R² and root mean square error (RMSE) of 0.53 and 0.002 1, respectively. PSRCM effectively corrected the spectral differences caused by characteristic signals at 6 824 cm^-1, 5 360 cm^-1, and 5 180 cm^-1, with correction effects significantly superior to those of standard normal variate transformation (SNV) and multiplicative scatter correction (MSC). The research result can provide a theoretical basis for the transfer correction method of straw near-infrared spectroscopy models, which can be applied without the need for standard samples.

Wheat, as one of the world's essential food crops, requires timely diagnosis and control of diseases and pests to significantly reduce yield losses, which is crucial for global food security. However, deep learning methods typically rely on large amounts of training data and high-performance computing resources, which pose limitations in scenarios with small sample sizes and limited resources. To address these issues, a knowledge-enhanced lightweight multi-modal wheat diseases and pests identification model named multi-modal blend convolutional neural network (Blend-CNN) was proposed. The model was structured around a dual-branch convolutional neural network framework. It utilized an EfficientNet backbone network to extract image features of diseases and pests and incorporated a multi-branch TextCNN backbone network to extract textual features of diseases and pests descriptions, thereby obtaining more feature information and improving identification accuracy. Additionally, an innovative convolutional network-based multi-modal fusion method was introduced, allowing the model to optimally integrate information from both modalities globally. Furthermore, to mitigate the accuracy loss common in traditional multi-modal methods, the gradient blend loss function was developed. Finally, to verify the model's effectiveness, comparative experiments were conducted on a constructed dataset containing 880 samples. The results demonstrated that the proposed model achieved the highest identification accuracy of 96.95% on this dataset, compared with other models, it had fewer parameters, lower complexity, and it was more lightweight which was applicable for edge devices, offering theoretical support for wheat diseases and pests identification in scenarios with limited sample sizes and resources.

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.