In the plot combine harvester, the impurity removal system based on the Coanda effect serves as a novel secondary cleaning device, featuring advantages of simple structure and compact spatial footprint. However, in practical applications, relying solely on the Coanda effect generated by curved surfaces can only achieve partial impurity separation, as most impurities fail to be effectively removed under airflow, resulting in suboptimal cleaning efficiency. To enhance the device's performance, strengthen the Coanda effect intensity at the end of the curved surface, and mitigate airflow diffusion, an auxiliary plate structure was introduced to the original design. Using the CFD-DEM method, the particle deflection angles of wheat grains and husk impurities were obtained for the configuration without the auxiliary plate. These angles were then utilized to determine the variation range of the auxiliary plate's free-end angle (4.89°~27.12°) through geometric relationships. Numerical simulations were employed to analyze the flow field structure within the Coanda effect zone of the device, identifying the height range (0.112~0.128 m) where abrupt velocity changes occurred at the end of the Coanda effect. Based on simulation results, the optimal length of the auxiliary plate and the positioning of its fixed end were further refined. Bench tests demonstrated that the highest cleaning efficiency was achieved when the auxiliary plate had a free-end angle of 25°, a length of 155 mm, and a fixed-end height of 0.122 m, and the impurity content in the grain is 0.998%, conclusively validating its significant role in improving device performance.

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 the challenge faced by sugarcane harvester operators who rely solely on visual judgment and experience to determine cane tops' positions, making it difficult to adjust cutting height accurately and in real-time, an intelligent positioning system for sugarcane harvester top cutters was proposed based on dual-modal and fuzzy adaptive PID control. Additionally, a cluster-based knife adjustment strategy was developed based on sugarcane cluster feature recognition. The system firstly utilized the YOLO v8 instance segmentation model to detect cane tops, with a depth camera capturing depth data of sugarcane tops in real time and converting pixel coordinates into camera coordinates for height measurement. To validate the depth camera's field performance, multiple field experiments were conducted. Results showed that within a range of 50~100 cm from the sugarcane, the average relative error of the depth camera ranged from 0.189% to 0.949%. Subsequently, fuzzy rules were designed, and a microcontroller integrated with a fuzzy adaptive PID algorithm was used to control the servo motor's operating speed. The maximum rising response speed was approximately 289.36 mm/s, and the descending response speed was about 273.16 mm, with a measurement error within±1 mm. PID comparison experiments showed that fuzzy PID control, compared with traditional PID control, reduced the response time by 0.12 s, bringing it down to 1.24 s. The overshoot was decreased from 12.80 mm to 4.63 mm, the number of overshoots dropped to one, and the steady-state error stabilized within±2 mm. Finally, dynamic tests demonstrated that the system's average recognition time was 0.08 s, showcasing excellent real-time performance.

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.

The existing residual plastic film recovery techniques in Xinjiang's cotton fields primarily focus on mechanical properties, neglecting the dragging effect of root stubble on the film during collection. This results in residual film readily becoming entangled in the root stubble. It confirmed the operational parameters of the pick-up teeth by analyzing the recovery of residual film when picking up teeth in conditions containing root stubble. Employing a coupled finite element method (FEM) and smooth particle hydrodynamics (SPH) algorithm, the tines, stubble, and residual film were modeled as FEM meshes, while the soil was represented as SPH particles. This enabled simulation of the shear strain, peak stress, pick-up height, and tensile stress exerted by root stubble on the residual film. The recovery of residual plastic film in the presence of cotton root stubble was simulated and the recovery efficiency was analyzed. Field trial results indicated that when root stubble height ranged from 5 cm to 9 cm, the average recovery rate of residual film reached 91.25%, with a residual film retention rate on root stubble of 4.30%. When root stubble height was 10~12 cm, the average residual film recovery rate across plots was 82.66%, with 12.55% of residual film remaining on the root stubble. Plots with root stubble heights of 5~9 cm exhibited an 8.59 percentage point increase in recovery rates compared with plots with root stubble heights of 10~12 cm, alongside an 8.25 percentage point reduction in residual plastic film on stubble. In trial plots with root stubble heights of 5~9 cm, the residual film retention rate on root stubble was 45.34% lower than in plots with root stubble heights of 10~12 cm. The research findings can provide theoretical support for the correlation between root stubble and plastic film remnants, as well as for enhancing the recovery rate of plastic film remnants.

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 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 explore the suitability of abandoned cultivated land for recultivation and improve the utilization rate of cultivated land, taking Lishu County, Jilin Province as the study area, firstly based on the Google Earth Engine platform and the 2022 Sentinel-2 image data of Lishu County, a total of 46 features such as spectral features, vegetation features, texture features, and terrain features were calculated, and random forest algorithm was used to construct the optimal feature set, and finally 39 features were screened to participate in the classification, so as to obtain the scope of abandoned cultivated land. Then the AHP-CRITIC-TOPSIS model was used to construct an evaluation model of the suitability of abandoned cultivated land for recultivation from three dimensions: natural, soil and socio-economic conditions, and the suitability was divided into four grades: highly suitable, moderately suitable, barely suitable and unsuitable, so as to put forward a feasible scheme for the rehabilitation of abandoned cultivated land. The results showed that the accuracy of the classification results of land use types in Lishu County in 2022 was relatively accurate, and the overall classification accuracy and Kappa coefficient were above 95%, indicating that the classification results were consistent with the actual land types and the overall classification effect was good. The area of abandoned cultivated land not suitable for recultivation in Lishu County was 1 673.73 hm², which was mainly located in the southeast of the study area, and the area of abandoned cultivated land suitable for recultivation was 1 071.67 hm², which was mainly located in the low-altitude plain area in the northwest of the study area. At present, the implementation of the national cultivated land protection policy indeed improved the use of cultivated land in rural areas, but the problems of abandonment and low utilization efficiency of rural cultivated land were still prominent, and the results can provide a scientific basis for improving the quality of cultivated land and ensuring food security in Lishu County and surrounding areas.

The human-land-water systems within a river basin are interconnected and exhibit mutually reinforcing relationships. Investigating their coupling coordination degree and influencing factors is crucial for achieving regional high-quality development and ecological civilization construction. Taking the nine provinces in the Yellow River Basin as the study area, the entropy method and a comprehensive evaluation method were employed to measure the index levels of the human system, land system, and water system within the Yellow River Basin from 2013 to 2022. Building upon this foundation, the coupling coordination degree model and fixed effects (FE) model were utilized to analyze and evaluate the spatio-temporal differentiation characteristics of the human-land-water systems coupling coordination degree and its influencing factors at both dual (human-land, human-water, land-water) and ternary (human-land-water) levels. The results indicated that from 2013 to 2022, the overall human-land-water systems in the nine provinces showed steady progress, although the development speeds of the subsystems varied, mostly exhibiting a fluctuating upward trend. Significant spatial disparities existed in the coupling coordination degree across the nine provinces during 2013—2022. By 2022, five provinces (Inner Mongolia, Ningxia, Shanxi, Henan, Shandong) reached an intermediate coordination level, while the other four provinces still had considerable room for development. The coupling coordination degrees of the dual systems (human-land, human-water, land-water) in all provinces also generally showed a year-by-year strengthening trend. Technological investment and rural governance investment positively influenced the coupling coordination degree. Industrial structure upgrading, the degree of openness, fixed asset investment, and government regulatory capacity exerted varying degrees of influence on the coupling coordination degree. In conclusion, against the backdrop of advancing the major national strategy for ecological protection and high-quality development in the Yellow River Basin, differentiated pathways for synergistic human-land-water development should be formulated for the upper, middle, and lower reaches. This would facilitate the rational coordination of multi-system development relationships within the basin and promote healthy and sustainable regional development in the future.

Accurately and rapidly monitoring the spatio-temporal distribution of aboveground biomass in crops is crucial for assessing growth conditions and managing precision irrigation in agricultural fields. Spectral indices have been widely utilized for estimating aboveground biomass; however, the spectral saturation effect under high coverage significantly impacts model performance. Furthermore, the effectiveness of integrating complementary and synergistic effects of spectral, temperature, and texture information from UAV remote sensing for estimating maize aboveground biomass across different growth stages and irrigation treatments at the farmland scale remains unclear. Additionally, the influence of meteorological factors on the interannual performance of remote sensing models for aboveground biomass estimation requires further investigation. Based on data from experimental sites in Inner Mongolia in 2018, 2019, and 2021, the spectral indices, temperature indices, and texture information obtained from UAV-based multisource remote sensing system, along with meteorological parameters such as reference evapotranspiration and vapor pressure deficit, were used as input features. Five modeling methods—stepwise regression, random forest regression, adaptive boosting regression, support vector regression, and one-dimensional convolutional neural network regression—were employed to establish a multisource feature fusion remote sensing estimation model for aboveground biomass of maize in large fields. The results showed that compared with single-type remote sensing information, the aboveground biomass estimation model integrating spectral, temperature, and texture information had better accuracy. Among them, the one-dimensional convolutional neural network regression model had relatively better accuracy (R²=0.87, RMSE=295.77 g/m²). After introducing meteorological parameters, there was no significant improvement in the accuracy of the aboveground biomass estimation model by using drone multisource remote sensing information, indicating redundancy between meteorological parameters and drone multisource remote sensing information. Considering the cost of drone-mounted equipment and the operation efficiency, the aboveground biomass estimation model based on spectral indices and meteorological parameters using random forest regression also had satisfactory accuracy (R²=0.85, RMSE=296.74 g/m²), suggesting that meteorological parameters can serve as a supplement to spectral indices. The performance of the one-dimensional convolutional neural network model for aboveground biomass estimation varied across different growth stages, indicating that the accuracy and reliability of the model need to be comprehensively assessed based on the crop growth stage. The research result can provide technical support for promoting the application of drone multisource remote sensing technology in precision irrigation management in farmlands.