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Abstract A methodology combining physical experiments with simulation was employed to acquire contact parameters of sandy soil precisely for planting tiger nuts in the desert area of Xinjiang. The stacking angle under different parameter combinations was applied as a response value. Through the Plackett–Burman test, several factors that have a significant influence were determined. The steepest ascent test was conducted to establish the finest scope of values for these parameters. The stacking angle was considered the response variable, and non-linear tools were used to optimize these parameters for simulation. The findings showed that applying response surface methodology (RSM) resulted in a relative error of 1.24%. In the case of BP-GA, the relative error compared to the physical test value was 0.34%, while for BP, it was 2.18%. After optimization using Wavelet Neural Network (WNN), the relative error was reduced to only 0.15%. Results suggest that WNN outperforms the RSM model, and the sandy soil model and parameters generated using WNN can be effectively utilized for discrete element simulation research.

Assessing the quality of UAV-HSIs (Unmanned aerial vehicle hyperspectral images) is crucial for evaluating sensor performance, identifying distortion types, and measuring data inversion accuracy. Due to the absence of reference images, UAV-HSI quality assessment leans towards no-reference image quality assessment (NR-IQA), offering versatile applications. NR-IQA methods of remote sensing images using machine learning techniques have emerged, however, NR-IQA methods for UAV-HSIs containing multi-type and multiple distortions have not been developed. This paper introduces an NR-IQA method for UAV-HSI, employing machine learning techniques. We summarize and simulate distortion types in UAV-HSIs, constructing a quality assessment dataset based on 23 original high-quality and 806 simulated degraded UAV-HSIs. Extracting 129 features encompassing texture, color, transform domain, structural, and statistical aspects, we form seven feature sets through random and filtered feature selection algorithms. Ten machine learning quality assessment models are trained using this dataset and feature sets. The results showed that the model with the highest evaluation accuracy was extra trees (ET) (R2 = 0.928, RMSE = 0.326, RPD = 3.601), using feature set 1 that fuses Tamura texture, color, wavelet transform, and mean subtracted contrast normalized (MSCN) coefficient for a total of 11 features, the PLCC and SROCC of its predicted and true quality scores reached 0.963 and 0.925, respectively. In addition, the random forest (RF), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), and extreme learning machine (ELM) also had high evaluation accuracies (R2 > 0.9 and RPD > 2.5). These findings underscore the applicability of our proposed machine learning-based NR-IQA method to assess the quality of the UAV-HSIs containing noise, blur, strip noise, and multiple distortions. Additionally, this study serves as a reference for selecting features and models for other hyperspectral image quality assessments.

Addressing the issues of the sand cat swarm optimization algorithm (SCSO), such as its weak global search ability and tendency to fall into local optima, this paper proposes an improved strategy called the multi-strategy integrated sand cat swarm optimization algorithm (MSCSO). The MSCSO algorithm improves upon the SCSO in several ways. Firstly, it employs the good point set strategy instead of a random strategy for population initialization, effectively enhancing the uniformity and diversity of the population distribution. Secondly, a nonlinear adjustment strategy is introduced to dynamically adjust the search range of the sand cats during the exploration and exploitation phases, significantly increasing the likelihood of finding more high-quality solutions. Lastly, the algorithm integrates the early warning mechanism of the sparrow search algorithm, enabling the sand cats to escape from their original positions and rapidly move towards the optimal solution, thus avoiding local optima. Using 29 benchmark functions of 30, 50, and 100 dimensions from CEC 2017 as experimental subjects, this paper further evaluates the MSCSO algorithm through Wilcoxon rank-sum tests and Friedman’s test, verifying its global solid search ability and convergence performance. In practical engineering problems such as reducer and welded beam design, MSCSO also demonstrates superior performance compared to five other intelligent algorithms, showing a remarkable ability to approach the optimal solutions for these engineering problems.

This study aimed to enhance the efficiency of tiger nut combine harvesters by reducing impurity and loss rates during processing. Scholars focused on analyzing the composition and suspension speed of the bean mixture, leading to the development of a wind-screen impurity-removal method. The wind-screen-type bean-separation device was designed with a cross-flow fan, louver screen, frame, and driving mechanism. Theoretical analysis was employed to discuss the motion characteristics and behavior of the sieve body and material, thereby revealing the screening dynamics of tiger nuts and impurities. Factors such as crank radius, crank speed, and fan speed were identified as crucial for optimizing separation performance. Initial single-factor tests helped narrow down the range of influencing factors. Subsequently, a three-factor, three-level Box–Behnken test was conducted with crank radius, crank speed, and fan speed as variables and impurity rate and loss rate as evaluation indexes. This led to the establishment of a multiple regression equation linking these factors to the evaluation indexes. Through response surface analysis and multi-objective optimization using the regression model, the optimal operational parameters for the device were determined: crank radius of 45 mm, crank speed of 497 r/min, and the fan speed of 1100 r/min. Theoretical calculations predicted an impurity rate of 2.42% and a loss rate of 0.51%. Verification tests confirmed these findings, showing an average impurity rate of 2.53% and a loss rate of 0.56%, which met the mechanized harvesting standards for tiger nuts. Overall, this study introduces a novel method and technical framework for effectively separating tiger nuts from impurities, thereby advancing the mechanization of tiger nut harvesting processes.

To tackle problems such as the difficult separation from sand and the high power consumption of tiger nut harvesting in the sandy areas of Xinjiang, a conveying and separating device for tiger nut harvesters was designed. The axial and radial migrations of materials under screw action and the separation process of materials under vibratory action were analyzed dynamically. A simulation analysis was carried out on the conveying and separating process based on EDEM software. The migration trajectories of tiger nuts and sand particles were extracted, the displacement variations of sand particles on the X-axis, Y-axis, and Z-axis were analyzed in the action area of the screen-cleaning spike teeth and the screw action area, respectively, and the conveying and separation law of the tiger nut harvest mixture was clarified. With key parameters such as the screw velocity ratio, amplitude, vibration frequency, and machine operation velocity as test factors, and with the sand removal rate, crushing rate, and power consumption as test evaluation indicators, a four-factor, five-level orthogonal central composite test design was implemented. The test results were analyzed via the regression variance analysis method, and relation models between variable factors and evaluation indicators were constructed. The test results show that under the combined conditions of a screw velocity ratio of 0.88, an amplitude of 4.7 mm, a vibration frequency of 7.5 Hz, and a machine operation velocity of 0.92 km/h, the sand removal rate is 90.40%, the crushing rate is 1.66%, and the power consumption is 2.24 kW in theory. The optimized results were verified by tests. The sand removal rate was 88.92%, the crushing rate was 1.71%, the total power consumption was 2.29 kW, and the errors from the predicted values were 1.6%, 3.0%, and 2.2%, respectively, meeting the requirements for tiger nut harvesting conveyance and separation. This research can provide support for the development of technology and equipment for mechanized harvesting of tiger nuts in the sandy areas of Xinjiang.

Signal-to-noise ratio (SNR) is an important radiation characteristic parameter for remote sensing image quality assessment as well as a key performance indicator for remote sensing sensors. At present, the SNR estimation methods based on regular segmentation or continuous segmentation are generally used to obtain image SNR. However, the land cover type has a great influence on the results of the SNR estimation method using regular segmentation, especially the high spectral resolution and high spatial resolution remote sensing images obtained by the low-altitude UAV hyperspectral sensor. In addition, some land cover types are difficult to achieve continuous segmentation. In view of this limitation of the existing SNR estimation methods, a new unsupervised method for estimating SNR in UAV hyperspectral images has been developed in this article, called pure pixel extraction and spectral decorrelation. By directly extracting pure pixels in the spatial dimension and combining the correlation of the spectral dimension to obtain the SNR of the hyperspectral image, this new method replaces the conventional method of improving the segmentation algorithm to improve the accuracy of SNR estimation. Additionally, the box counting method is introduced to determine the image SNR aggregation interval. The results showed that the proposed method had higher accuracy and smaller errors than the other SNR estimation methods. Besides, this method had stronger robustness, it can be used for both radiance and reflectance (atmospherically corrected) UAV hyperspectral images.

Efficient furrow fertilization is extremely critical for fertilizer utilization, fruit yield, and fruit quality. The precise determination of trench quality necessitates the accurate measurement of its characteristic parameters, including its shape and three-dimensional structure. Some existing algorithms are limited to detecting only the furrow depth while precluding the tridimensional reconstruction of the trench shape. In this study, a novel method was proposed for three-dimensional trench shape reconstruction and its parameter detection. Initially, a low-cost multi-source data acquisition system with the 3D data construction method of the trench was developed to address the shortcomings of single-sensor and manual measurement methods in trench reconstruction. Subsequently, the analysis of the original point cloud clarified the “coarse-fine” two-stage point cloud filtering process, and then a point cloud preprocessing method was proposed based on ROI region extraction and discrete point filtering. Furthermore, by analyzing the characteristics of the point cloud, a random point preselection condition based on the variance threshold was designed to optimize the extraction method of furrow side ground based on RANSAC. Finally, a method was established for extracting key characteristic parameters of the trench and trench reconstruction based on the fitted ground model of the trench side. Experimental results demonstrated that the point cloud pretreatment method could eliminate 83.8% of invalid point clouds and reduce the influence of noise points on the reconstruction accuracy. Compared with the adverse phenomena of fitting ground incline and height deviation of the original algorithm results, the ground height fitted by the improved ditch surface extraction algorithm was closer to the real ground, and the identification accuracy of inner points of the ground point cloud was higher than that of the former. The error range, mean value error, standard deviation error, and stability coefficient error of the calculated ditch width were 0 ~ 5.965%, 0.002 m, 0.011 m, and 0.37%, respectively. The above parameters of the calculated depth were 0 ~ 4.54%, 0.003 m, 0.017 m, and 0.47%, respectively. The results of this research can provide support for the comprehensive evaluation of the quality of the ditching operation, the optimization of the structure of the soil touching part, and the real-time control of operation parameters.

To improve the accuracy of the discrete element research, physical and simulation experiments were used to calibrate the cotton stalk contact parameters. Based on the stalk-stalk and stalk-steel contact mechanics, the parameters were measured in physical experiments, and the discrete element simulation software was used to build the stalk model. In the simulation process, the Plackett-Burman experiment was used to screen three significant factors from six initial factors. The steepest Plackett-Burman experiment was used to determine the optimal interval of the significant factors. A second-order regression model of the significant factors and the angle of repose was established according to the Central Composite design experiment. The best parameter combination of the significant factors was then obtained: the coefficient of static friction on stalk-steel contact was 0.31, the coefficient of static friction on stalk-stalk contact was 0.62, and the coefficient of rolling friction on stalk-stalk contact was 0.02. The relative error between the physical angle of repose and the simulated angle was 3.27%, indicating that it is feasible to apply the simulation experiment instead of the physical one. It offers insights into cotton stalk contact parameter settings and film-stalk separation in the simulation.

The transformation and upgrading of traditional agriculture are required to address its shortcomings and deficiencies, which have resulted in environmental pollution or water problems. Precision agriculture emerged at the historic moment to solve the current problems. Field information collection, information management and decision-making, and execution systems are the three key links of precision fertilization and irrigation. The technical principle and application of field information acquisition systems are analyzed. The information management and decision-making system describes the management and summary of information in crop growth. The execution system combines the knowledge of various disciplines and experts for targeted applications to specific crops. It further focuses on the core implementation system, that is, variable fertilization technology and variable spraying technology that can realize variable operations. Major contributions from different countries, institutions, corresponding authors, and journals are presented in detail. This study proposes several suggestions and ideas based on the research status and progress of the three key systems to provide a theoretical basis and technical support for the research and development of key technologies and innovative devices of precision agricultural fertilization and irrigation.

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

In this study, to obtain the contact parameters of particulate materials accurately and quickly in residual film mixture after sieving, the contact parameters of particulate materials were calibrated via a physical test and simulation test. By using the self-made dynamic angle of a repose measurement test bench, the dynamic angle of repose of the particulate materials was measured at 41.32°, and the standard deviation was 1.33°. A discrete element simulation of the dynamic angle of the repose test was performed via an EDEM screening experiment design through a simulation of a combination of different parameters, with the dynamic angle of repose as the response value. Through simulation experiments, three significant influencing factors, as well as the level range of each factor, were confirmed. By using the response surface experiment, a mathematical model of the dynamic angle of repose and the three most influential parameters was created. The analysis of variance showed that the determination coefficient R2 and the correction determination coefficient R2adj were 0.9824 and 0.9598, respectively. The model had a good fit. The variable coefficient was 2.06% and the lack of fit was non-significant, which showed that the regression model was very significant, and the dynamic angle of repose could be predicted according to the model. By solving the optimization for the mathematical model, the optimal combination of parameters with three important influencing factors were obtained. The results showed that the coefficient of the static friction between soil and soil was 0.38, the coefficient of the rolling friction between soil and soil was 0.08, and the coefficient of the static friction between cotton residue and cotton residue was 0.33. The relative error of the dynamic angle of repose between the simulation with the optimal parameter combination and the physical test value was 2.64%. The results could provide a reference for the calibration of the discrete element model parameters of other agricultural particulate material, as well as provide a theoretical basis for the design of related collecting and conveying machinery.

Green, healthy and sustainable development is the development direction of China’s agriculture, and stable fertilizer has become the first choice for orchard fertilizer. In this paper, in order to improve the working performance of the combined fertilizer application device of 2KF-30 type orchard stable fertilizer deep applicator, the matching degree of the combined operation of scraper and auger was taken as the research objective, and theoretical analysis, numerical simulation, and experimental verification were combined to carry out the research. Applying fuzzy theory, the comprehensive qualified rate weighted by the uniformity of fertilization and the qualified rate of fertilization was proposed as the evaluation index. The scraper shaft speed, scraper space, spiral conveyor speed, and spiral conveyor pitch were selected as the test factors, and the comprehensive qualified rate of fertilization for the combined fertilization device was taken as the evaluation index. The test design was carried out by single-factor and response surface method tests, and numerical simulations were conducted by discrete element software to analyze the optimal combination of fertilizer application parameters. The test results were analyzed based on the numerical simulation of discrete element software. The testbed was set up to verify the above optimal results. The results showed that the maximum error between the physical test and the numerical simulation was 8.67%, the minimum error was 1.75% and the overall mean error between the physical test value of the combined fertilizer application pass rate and the predicted value of the quadratic regression equation was 5.99%, which can meet the operational performance requirements of the fertilization device. The research results can provide a theoretical basis for the design and improvement of the deep fertilizing machine for orchard manure.

Abstract Temperature stability was taken as the evaluation index of processing performance, and the three factors that influence normal milk processing and mixing performance were optimized by response surface analysis and BP‐GA neural network algorithm. Analysis results showed the influence order of the factors on temperature stability was as follows: shape > height > rotating speed. In the optimization by response surface methodology (RSM), when rotating speed was 30 r/min, height was 31 mm, and blade shape was a full trapezoid, predicted value and actual value of variable coefficient were 0.0046 and 0.0044 respectively, with relative error of 4.5%. In the optimization by BP‐GA neural network algorithm, when rotating speed was 34 r/min, height was 25 mm, and blade shape was a full trapezoid, the predicted value and actual value of variable coefficient were 0.0036 and 0.0035 respectively, with relative error of 2.9%. The predicted root‐mean‐square error of the model by the BP‐GA neural network algorithm was 0.0013, determination coefficient was 0.9960, and relative percent deviation was 8.4961, which showed better performance than the RSM model. Thus, the BP‐GA neural network algorithm has better fitting performance, and then, the optimal working parameter combination was confirmed, which could provide reference to improving double‐blade normal milk processing and mixing device design and milk processing quality.

The accuracy of the material parameter settings directly affects the reliability of the results of the discrete element method simulation. It is necessary to calibrate the relevant parameters to obtain accurate discrete element simulation results when separating the cotton stalk particles from the residual film after crushing. The repose angle of the chopped cotton stalk particles was used as the response value to calibrate the contact parameters between particles. Physical tests measured the intrinsic particle and contact parameters between the cotton stalk particles and the contact material, which provided data for the simulation tests. According to the biological structure characteristics of cotton stalk, the discrete element method model of cotton stalk particles was constructed by bonding the elements of nonequal-diameter basic particles. Based on the response surface methodology, the stacking test of particles was simulated. The response model between the contact parameters and repose angle was established, and the effect law of the single-factor terms and interaction terms on the repose angle was analyzed. The optimal combination of contact parameters was obtained through the single-objective and multi-variable optimization methods. Finally, the contact parameter combination was verified by a simulation test of the repose angle. The results showed that the average relative error of the repose angle between the simulation test and the physical test was 1.04%, which verified the accuracy of the calibrated contact parameters and the reliability of the simulation test. These parameters provide a basis for the discrete element simulation study of cotton stalk motion in the separation process of cotton stalks and residual film and the subsequent gas–solid coupling simulation research.

The friction characteristic parameters of cotton stalks are important basic physical parameters required to establish the crushing mechanics model and study the separation machinery of film residue mixtures. In this paper, the friction characteristics of cotton stalks were studied using response surface methodology, and the influence of the variation in contact materials, sampling location, and moisture content on the static sliding friction coefficient (μs) and stable static rolling angle (φs) were analyzed. The results show that the contact materials, sampling location, and moisture content significantly influence the μs and φs. When the contact material is cotton stalk bark, the μs and φs values of cotton stalks are maximum. When the sampling location of the cotton stalk changes from top to bottom, the μs increases and φs decreases, respectively, while the moisture content has an opposite influence on the μs and the φs. The model coefficient of the μs and the φs indicates that the influencing factors have a high degree of explanation for the influence on the μs and φs. This study on the friction characteristics of cotton stalks can provide theoretical references and basic parameters for the separation technology research and equipment development of film residue mixtures.

Low working efficiency is an important reason for the limited application of the traditional aspirated-air type jujube fruit pickup machine. In this study, a provoke-suction type harvester for ground jujube fruit (PSH) was designed, based on the principle of negative pressure suction after shoveling the jujube fruit mechanically. The main working parameters were analyzed and the structures of the key devices were designed. Then, a three-factor and three-level Box–Behnken method was used to evaluate the performance of the PSH. The results showed that the pickup rate, impurity rate, and working efficiency were 99.36%, 5.63%, and 1672.3 kg∙h−1, respectively; as the forward speed, provoke teeth buried depth, and airflow velocity were 0.21 kg∙h−1, 74 mm, and 26.4 kg∙h−1, respectively. Furthermore, the verification results showed that the pickup rate, impurity rate, and working efficiency were 98.05%, 5.97%, and 1591.2 kg∙h−1, respectively, moreover, the relative errors were 1.32%, 6.04%, and 4.85%, respectively, indicating that the parameter optimization model can accurately predict the test results. The working efficiency of the PSH was significantly improved compared with the traditional aspirated-air type jujube fruit pickup machine. This research can provide a reference for the development of the jujube fruit pickup machine.

Discrete element method (DEM) simulation is an important method to analyze the interaction relationship between materials and equipment, and to develop machinery and/or equipment. However, it is necessary to input specific simulation parameters when establishing a DEM simulation model. In this study, the interval values were measured through angle of repose tests of fallen jujube fruit (FJF), and the simulation angle of repose tests for FJF were established with EDEM software (DEM Solutions Ltd. Edinburgh, Scotland, UK). Then, the Plackett-Burman design, steepest ascent search experiment, and center composite design experimental methods were utilized to obtain the specific values of the simulation parameters from the interval values. The results showed that significant influencing factors in the simulation angle of repose include the Poisson’s ratio, the static friction coefficient between FJF, and the static friction coefficient between FJF and the steel plate, for which the optimal values were 0.248, 0.480, and 0.309, respectively. The angle of repose tests’ results showed that the error was 0.53% between the simulation angle of repose (29.69°) and the angle of repose (29.85°). In addition, the flow rate test results showed that the average error was 5.84% between the physical and simulation tests. This indicated that the calibrated parameters were accurate and reliable, and that the simulation model can accurately represent the physical tests. Consequently, this study provides an EDEM model of FJF that was essential in designing machinery and equipment through the EDEM simulation method.

Blanching is an essential step before processing of agricultural products as it can inactivate enzymes that cause undesirable changes. In current work the effects of superheated steam impingement blanching (SSIB) time and temperature on drying characteristics and red pigments content of line pepper were investigated. Results showed that after a 3-min SSIB pretreatment at 110 °C the pepper epidermis covered with wax coat was damaged. In addition, the drying time was extensively decreased and the loss of red pigments of dried products was reduced. Results showed that the whole drying process took place in the falling rate period, which indicated that diffusion was the dominant physical mechanism governing moisture movement in the samples. Therefore, the second Fickâs law of diffusion was used to determine the effective moisture diffusivity (Deff) of line pepper, which increased from 1.193 Ã 10â10 to 3.128 Ã 10â10 m2/s with increasing of the drying temperature and air velocity. The drying activation energy (Ea) of pretreated samples was 34.31 kJ/mol, which decreased by 3% compared with the non-pretreatment group. The findings of this work indicate that SSIB is very promising pretreatment technique as it not only enhances drying process but also improves bioactive substance preservation of red pepper. Keywords: Line pepper, High-humidity hot air impingement blanching, Air impingement drying, Drying characteristics, Quality attributes

As one of the effective ways of reducing the cost and increase the revenue, feeding dairy cows with individual precision in quantity could further develop the production potential of high- yield dairy cows and improve the milk yield of every dairy cow. Therefore, based on three kinds of technologies of radio frequency identification technology, wireless transmission technology and infrared technology and grounded on the foundation of previous studies, this thesis has completed the track- type equipment for feeding dairy cows with individual precision in quantity. The equipment takes the computer as the information management platform, adopts the singly chip microcomputer as the control platform, uses the wireless module and U disk to conduct the transmission of individual feeding data of dairy cows and guides the whole process with the track. This kind of equipment could advance automatically, identify and feed dairy cows precisely during the feeding process. This thesis also designs the matching feeding technology of the equipment, and accomplishes the test of feeding outside the cowshed, which takes “TMR + precise feeding with concentrated feed” as the feeding model, so that the feeding process could be more effective and further steady.

Nowadays, with the rapid development of quantitative remote sensing represented by high-resolution UAV hyperspectral remote sensing observation technology, people have put forward higher requirements for the rapid preprocessing and geometric correction accuracy of hyperspectral images. The optimal geometric correction model and parameter combination of UAV hyperspectral images need to be determined to reduce unnecessary waste of time in the preprocessing and provide high-precision data support for the application of UAV hyperspectral images. In this study, the geometric correction accuracy under various geometric correction models (including affine transformation model, local triangulation model, polynomial model, direct linear transformation model, and rational function model) and resampling methods (including nearest neighbor resampling method, bilinear interpolation resampling method, and cubic convolution resampling method) were analyzed. Furthermore, the distribution, number, and accuracy of control points were analyzed based on the control variable method, and precise ground control points (GCPs) were analyzed. The results showed that the average geometric positioning error of UAV hyperspectral images (at 80 m altitude AGL) without geometric correction was as high as 3.4041 m (about 65 pixels). The optimal geometric correction model and parameter combination of the UAV hyperspectral image (at 80 m altitude AGL) used a local triangulation model, adopted a bilinear interpolation resampling method, and selected 12 edgemiddle distributed GCPs. The correction accuracy could reach 0.0493 m (less than one pixel). This study provides a reference for the geometric correction of UAV hyperspectral images. [ABSTRACT FROM AUTHOR]

To improve the accuracy of the discrete element research, physical and simulation experiments were used to calibrate the cotton stalk contact parameters. Based on the stalk-stalk and stalk-steel contact mechanics, the parameters were measured in physical experiments, and the discrete element simulation software was used to build the stalk model. In the simulation process, the Plackett-Burman experiment was used to screen three significant factors from six initial factors. The steepest Plackett-Burman experiment was used to determine the optimal interval of the significant factors. A second-order regression model of the significant factors and the angle of repose was established according to the Central Composite design experiment. The best parameter combination of the significant factors was then obtained: the coefficient of static friction on stalk-steel contact was 0.31, the coefficient of static friction on stalk-stalk contact was 0.62, and the coefficient of rolling friction on stalk-stalk contact was 0.02. The relative error between the physical angle of repose and the simulated angle was 3.27%, indicating that it is feasible to apply the simulation experiment instead of the physical one. It offers insights into cotton stalk contact parameter settings and film-stalk separation in the simulation. [ABSTRACT FROM AUTHOR]

The influence of the variation in water content, sampling location and loading speed on the shearing and across-compression mechanical characteristics of dwarf-denseearly major cotton variety stalks was analysed. The second-order response models of the factors vs. the maximum shear force and critical across-compression failure force were built using the response surface methodology. Besides, the sampling location and water content have a significant influence over the maximum shear force and critical across-compression failure force, especially the sampling location. Both the maximum shear force and critical across-compression failure force reach a theoretical maximum when the water content is 10%, the sampling location is the lower part and the loading speed is 40 mm min-1. When the sampling location is the lower part, optimization is carried out assuming the theoretical minimum of the maximum shear force and critical across-compression failure force as the optimization target. In this case, the loading speed is 20 mm min-1, while the water content is between 30 and 50%. This indicates that the parameter range requires a low power consumption during the crushing of film residue mixtures. The test data can provide some theoretical references for the technical research and equipment development for the crushing of film. [ABSTRACT FROM AUTHOR]