Enhancing resource utilization: A novel method for effective separation of residual film and impurities in cotton fields.

This study addresses the challenge of incomplete separation of mechanically recovered residual films and impurities in cotton fields, examining their impact on resource utilization and environmental pollution. It introduces an innovative screening method that combines pneumatic force and mechanical vibration for processing crushed film residue mixtures. A double-action screening device integrating pneumatic force and a key-type vibrating screen was developed. The working characteristics of this device were analyzed to explore the dynamic characteristics and kinematic laws of the materials using theoretical analysis methods. This led to the revelation of the screening laws of residual films and impurities. Screening tests were conducted using the Central Composite Design method, considering factors such as fan outlet, fan speed, vibration frequency of the screen, and feeding amount, with the impurity-rate-in-film (Q) and film-content-in-impurity (W) as evaluation indexes. The significant influence of each factor on the indexes was determined, regression models between the test factors and indexes were established, and the effect laws of key parameters and their significant interaction terms on the indexes were interpreted. The optimal combination of working parameters for the screening device was identified through multivariable optimization methods. Validation tests under this optimal parameters combination showed that the impurity-rate-in-film was 3.08% and the film-content-in-impurity was 1.94%, with average errors between the test values and the predicted values of 3.36% and 5.98%, respectively, demonstrating the effectiveness of the proposed method. This research provides a novel method and technical reference for achieving effective separation of residual film and impurities, thereby enhancing resource utilization.

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools.

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.

Cotton stubble detection based on wavelet decomposition and texture features.

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.

Experimental study on microstructure and mechanical properties of stalk for Glycyrrhiza Glabra.

In this paper, a year-old stalk of Glycyrrhiza glabra was used as the research object. The electronic universal testing machine was used to test the mechanical properties of shearing and bending. The microstructure of the stalk of Glycyrrhiza glabra was observed with a microscope. Mechanical test research indicated that the shearing process included an elastic phase, a yield phase, and a plastic deformation phase. The bending process was divided into elastic deformation stage and plastic deformation stage. In addition, the shearing force, shearing energy, bending force and bending energy all increased with the increase in diameter. As the water content increased, the shearing force and bending force decreased at first, reached the minimum when the water content was about 45%, and then had an upward trend. The shearing energy increased with the water content, and the bending energy, decreased with the water content. The two test factors were statistically significant for both shearing and bending properties. The microscopic test results showed that the phloem, fiber, and pith constitute the microstructure of the licorice stalk. The linear regression model could reflect the correlation between the cross-sectional area of each part and the shearing force and bending force (P < 0.05). Through analysis, it was concluded that the change of the cross-sectional area of the stalk microstructure had an important influence on the mechanical properties of shearing and bending. The results can provide theoretical basis for the design of Glycyrrhiza Glabra stalk harvesting, crushing and processing equipment.

Parameter optimization of double-blade normal milk processing and mixing performance based on RSM and BP-GA.

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.