As the existing residual film crushing device in Xinjiang cannot directly crush membrane-impurity mixed material, by analyzing the compressive and cutting force characteristics of residual film material layer and cotton stalk, the cutting conditions of mixed materials were obtained, and the method of cutting was determined. A multi-edge toothed cutters was created, and a cutting device was built. It was preliminarily determined that the number of teeth in the cutters was 8, the clearance between the teeth and between the tooth and fixed blade was 3 mm, the speed of the high-speed cutter was 800 r/min, and the speed difference between the high- and low-speed cutters was –300 r/min. Test results show that the ratio of residual film to total residual film sampling mass was (2.22±0.3)%, (19.19±2.02)%, (58.94±3.19)% and (20.65±2.05)%, respectively, when the maximum outer profile size in the range of [0,20) mm, [20,100) mm, [100,500) mm and [500,~) mm. The mass of cotton stalks with lengths of [0,50) mm, [50,100) mm and [100,~) mm accounted for (32.57±1.5)%, (27.77±1.3)% and (39.66±1.75)%, respectively, and the cutting power consumption was (85.41±15.63) kJ. The test results can provide a basis for the subsequent membrane-impurity mixed material cutting technology, as well as some guidance for the separation of it. Keywords: multi-edge toothed cutter; membrane-impurity mixed material; cutting device; cutting conditions DOI: 10.25165/j.ijabe.20231602.7240 Citation: Liang R Q, Zhang B C, Chen X G, Meng H W, Wang X Z, Shen C J, Li Y P, Kan Z. Design and test of a multi-edge cutting device for membrane-impurity mixed material. Int J Agric & Biol Eng, 2023; 16(2): 73-84.

The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI), to estimate the Leaf Area Index (LAI) and above-ground biomass. For LAI estimation using the NDVI, the power function model (y = 10.083x^11.298) demonstrated higher precision, with a multiple correlation coefficient of R² = 0.8184 and the smallest root mean square error (RMSE = 0.3613). These results confirm the strong predictive capacity of NDVI for LAI, with the power function model offering the best estimation accuracy. In estimating above-ground biomass using RVI, the power function model of y = 6.5218x^1.33917 achieved the higher correlation (R² = 0.8851) for fresh biomass with an RMSE of 0.1033, making it the most accurate. For dry biomass, the exponential function model (y = 9.1565 × 10⁻⁵∙exp(1.1146x)) was the most precise, achieving an R² value of 0.8456 and the lowest RMSE value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring.

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

Hydraulic soil insertion device is a key component of orchard gas explosion subsoiling and fertilizing machine to realize rod fixed point soil insertion and gas fertilizer injection into soil. In order to explore the influence of the main working parameters and structural parameters on the depth and cylinder pressure of the hydraulic insertion device during the insertion process, the working parameters were optimized to ensure the insertion quality and efficiency. In this paper, force analysis was performed on the rod insertion process, and key parameter equation of soil insertion resistance was established. LS- DYNA finite element simulation software was applied to analyze the force variation of the rod during the insertion process. Box-Behnken test optimization design method and Design-Expert V8.0.6.1 software were used to carry out parameter optimization test of hydraulic insertion device. A multivariate quadratic polynomial regression equation was established by setting the engine revolution, insertion rod diameter and insertion time as independent variables, and the operation parameters of the hydraulic insertion device were optimized based on the relationship between the independent variables and the response values. The results showed that the regression equation model based on the response values of insertion depth and cylinder pressure had a good fitting degree. The engine revolution, rod diameter and insertion time all had significant effects on the increase of insertion depth and decrease of cylinder pressure, with interaction between the engine speed and insertion time with the insertion depth, and interaction between any two factors of engine revolution, rod diameter and insertion time with the cylinder pressure. The influences of the test factors on the insertion depth showed a descending order as engine speed, insertion time, and rod diameter. The influences of the test factors on the cylinder pressure showed a descending order as engine speed, rod diameter, and insertion time. Based on the results of insertion depth and cylinder pressure, the optimal combination of parameters was as follows: engine revolution of 1450 r/min; rod diameter of 32 mm; and the insertion time of 8 s. Under this optimal combination, the insertion depth of the hydraulic insertion device was 44.43 cm, and the cylinder pressure was 23.09 MPa. The experimental results showed that the optimal combination of parameters could meet the agronomic requirements of fast and deep insertion, thus providing a theoretical support for the improvement and optimization of hydraulic soil insertion device of gas explosion subsoiling and fertilizing machine. Key words: orchard management machinery, subsoiling, gas explosion, fertilization, hydraulic insertion, optimization DOI: 10.25165/j.ijabe.20231602.7126 Citation: Shen C J, Zhang L X, Jia S X, Zhou Y, Li F, Dai Y M, et al. Parameter optimization and test of hydraulic soil insertion device of orchard gas explosion subsoiling and fertilizing machine. Int J Agric & Biol Eng, 2023; 16(2): 132–141.