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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.

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]

The threshing mixture of Cyperus esculentus (Tiger nut) planted in sandy areas is complex during harvesting, and there are problems of high impurity rate and high cleaning loss rate of the seeds. In this paper, the threshing mixture of Cyperus esculentus is taken as the research object, and the suspension speed of each component is measured to design an “airflow + vibration” sieving device. Use the vector polygon method to explore the law of motion of the vibrating sieve, establish the force model of the seeds on the sieve surface, and analyze the conditions for the seeds to penetrate the sieve. According to the principle of seeds penetrating the sieve and long stalks not penetrating the sieve, the geometric model of sieve blades opening degree adjustment mechanism is constructed. Study of the conditions under which long stalks can be thrown out of the machine. The experiment bench of the cleaning device was built, and the evaluation indexes were the rate of seed impurity and the rate of cleaning loss of the device, and the crank speed, sieve blades opening degree and fan speed were used as the experiment factors to study the influence law of each influencing factor on the evaluation index through single-factor experiment. Using response surface methodology to find the optimal combination of working parameters of the cleaning device. To verify the device’s application on the combine harvester to improve the effectiveness of the cleaning operation of the Cyperus esculentus combine harvester. [ABSTRACT FROM AUTHOR]