Fertilization stability is an important index for evaluating the operational performance of variable fertilizer dischargers. To study the influence law of the combination of fertilizer discharge wheel rotational speed n and opening L on the fertilizer discharge performance, this paper firstly constructs a fertilizer amount prediction model based on a radial basis function neural network (RBFNN) through a calibration test, and after verification, its determination coefficient reaches 0.99965 with a mean relative error (MRE) of 3.88%. Then the discrete element simulation software (EDEM) was used to simulate the fertilizer discharge process under different control sequences for each of the three target fertilizer application amounts. The simulation results show that at the target fertilizer discharge rate of 944.92 g/min, when the control sequence is 18.3 r/min and 25 mm, the uniformity coefficient of variation (CV) of fertilizer discharge is the smallest. In the other control sequences, σ was higher than 20%, the stability of fertilizer discharge was poor, and the phenomenon of broken strips appeared; under the target fertilizer discharge rate of 2101.47 g/min, σ was the smallest at (24.2 r/min, 45 mm) 4.34%; under the target fertilizer discharge rate of 3842.87 g/min, σ was less than 4% in all cases, and at the control sequence (44.7 r/min, 45 mm), σ reached a minimum of 2.01%. Finally, using the simulation results and the prediction model of fertilizer amount based on RBFNN, the optimization model of fertilizer discharge control sequence based on the differential evolutionary (DE) algorithm was established, and a bench test was conducted to verify the optimization results, which showed that the accuracy and uniformity of fertilizer discharge met the operational requirements.

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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.

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.