The lack of accurate simulation model parameters in the optimization design process of variable fertilizer application devices has resulted in large errors between simulation and theoretical calculation results, which has restricted the development of variable fertilizer application devices to a certain extent. Additionally, there are few scholars studying urea granules, so many parameters of urea granular fertilizer cannot be directly obtained from the literature. The aim of this study is to calibrate a set of simulation parameters by combining physical and simulation tests. In this study, intrinsic parameters were systematically determined, including the particle size, particle density, elastic modulus, Poisson’s ratio and their static friction coefficients, rolling friction coefficients and restitution coefficients of urea particles. By performing the urea particle stacking test, the static friction coefficient between urea particles was calibrated to 0.27, and the rolling friction coefficient between particles was 0.11. To check the reliability of the calibration parameters, the simulation and physical tests of the repose angle and bulk density of urea particles were compared, and the results show that the relative error of repose angles and bulk density of urea particles was 0.78% and 1.19%, respectively. Through the simulation of the mechanical variable fertilizer discharger and the comparison test of the benchtop fertilizer discharging performance, the maximum relative error between the simulation and physical test fertilizer discharge is 3.69% when the working length of the outer sheave is 25 mm; the maximum relative error between the simulation and physical test fertilizer discharge is 3.39% when the working length is 35 mm; the maximum relative error between the simulation and physical test fertilizer discharge is 6.86% when the working length is 45 mm; the maximum relative error between the simulation and physical test fertilizer discharge is 4.95% when the working length is 55 mm. The maximum relative error between the simulated and physical test fertilizer discharge was 6.86% at 45 mm opening and 4.95% at 55 mm opening, and the results show that the urea particle calibration parameters are reliable. The results of this study can provide a theoretical reference for the optimization design and simulation study of variable fertilizer application devices.

The rapid and accurate detection of soil nutrient content through spectral technology is one of the requisite technologies for precision fertilization, which, however, is an unsolved issue. In order to achieve this purpose, a more robust and accurate model is established in this study. The regression algorithm is integrated with effective wavelength selection to construct the prediction model for total nitrogen, available phosphorus, and available potassium (N, P, and K), which removes the need for complex pretreatment and algorithm constraints. According to the research results, with regard to the prediction of soil nitrogen, phosphorus, and potassium contents, the joint interval support vector regression (Si-SVR) model performed best in modeling, with the root mean square error of prediction (RMSEP) limited to 0.0231%, 1.0554%, and 3.4225%, respectively. In addition, the relative percentage deviation (RPD) values were restricted to 2.68, 2.12, and 2.37, respectively. As indicated by the prediction results obtained for the above three nutrient contents, the RPD values of the Si-SVR model prediction accuracy evaluation indicators exceeded 2.0, which evidences a high level of prediction accuracy. This method makes it possible for spectral data to be applied in practical production, and these results provide a valuable reference for the effective detection of major soil nutrients.

The three-blade combined agitator consists of two propulsion blades of the same type (including planar propeller blades b, δ = 36.87°) and a curved blade (θ = 30°). Using numerical simulation methods, the power characteristics, flow field distribution, turbulence characteristics and dead zone percentage of two kinds of three-blade combined agitators (TBCAs) from laminar flow to turbulent flow in a mixing vessel were studied. Moreover, the torque measurement method was used to perform experimental verification. The results show that the predicted power curve is consistent with the experimental results. The fluid velocity near the propeller blades in the TBC-B type agitator (δ = 36.87°) is significantly high, and the maximum increase of the total velocity can reach 30.3%. The fluid flow velocity near the curved blades is increased, and the radial diffusion ability of the fluid at the bottom of the stirring vessel is enhanced. When mixing low-viscosity fluids, the TBC-B type agitator can increase the fluid velocity near the paddle area, with a maximum increase of 22.1%. The vertical combination of curved blades and planar propeller blades can effectively reduce the tangential velocity and increase the axial and radial velocities. When stirring high-viscosity fluids, the speed of the TBC-B type agitator in the near paddle area and far end of the blade is higher than that of the TBC-A type agitator. Under the same conditions, the TBC-B-type agitator exhibits superior fluid discharge performance and can be used in a wider range of viscosities. When Re = 44,910, the dead zone percentage of the TBC-A type agitator is 0.0216. The percentage of dead zones produced by the TBC-B-type agitator is smaller, and the mixing effect is superior to that of the TBC-A-type agitator.

In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative detection research. Through correlation spectroscopy (COS), a total of 882 representative samples from the seedling stage, bud stage, and flowering and boll stage were used for feature wavelength screening, resulting in 213 selected feature wavelengths. Based on all wavelengths and selected feature wavelengths, a backpropagation neural network (BPNN), a backpropagation neural network optimized by genetic algorithm (GA-BPNN), a backpropagation neural network optimized by particle swarm optimization (PSO-BPNN), and a backpropagation neural network optimized by sparrow search algorithm (SSA-BPNN) prediction models were established for cotton leaf chlorophyll content, and model performance comparisons were conducted. The research results indicate that the GA-BPNN, PSO-BPNN, and SSA-BPNN models established based on all wavelengths and selected feature wavelengths outperform the BPNN model in terms of performance. Among them, the SSA-BPNN model (referred to as COS-SSA-BPNN model) established using 213 feature wavelengths extracted through correlation analysis showed the best performance. Its determination coefficient and root-mean-square error for the prediction set were 0.920 and 3.26% respectively, with a relative analysis error of 3.524. In addition, the innovative introduction of orthogonal experiments validated the performance of the model, and the results indicated that the optimal solution for achieving the best model performance was the SSA-BPNN model built with 213 feature wavelengths extracted using the COS method. These findings indicate that the combination of hyperspectral data with the COS-SSA-BPNN model can effectively achieve quantitative detection of cotton leaf chlorophyll content. The results of this study provide technical support and reference for the development of low-cost cotton leaf chlorophyll content detection systems.

Based on a combination of physical and simulation experiments, a set of basic physical characteristic parameters and contact parameters suitable for granular fungal fertilizers were calibrated, which up to now have received little attention. The physical morphological characteristic parameters (overall dimension, 1000 grain weight, density, and moisture content), mechanical characteristic parameters (Poisson’s ratio, elastic modulus, and shear modulus), and contact parameters (static friction coefficient, rolling friction coefficient and recovery coefficient) of particles were measured through physical experiments. The parameters were screened for significance using the Plackett–Burman test. The results showed that the impact recovery coefficient, static friction coefficient, and rolling friction coefficient for the granular fungal fertilizer particles had a significant effect on the repose angle. The best range for the three parameters was determined using the steepest climbing test. The second-order regression model was optimized using the Box–Behnken design test. The relative error between the simulated repose angle (21.74°) and the physical test repose angle (21.84°) was small (0.50%). These optimal parameters obtained by calibration can provide a basis for the simulation analysis and parameter optimization of variable-rate fertilization systems.

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production.