To improve the operational efficiency of a hammer mill and delve into a high-efficiency, energy-saving grinding mechanism, the crucial parameters influencing the grinding of corn straw were identified as the spindle speed, hammer–sieve gap, and sieve pore diameter. According to the force analysis and kinematics analysis, the key factors affecting corn straw grinding were the spindle speed, the hammer–sieve gap, and the sieve pore diameter. The grinding process of corn straw was studied using computational fluid dynamics (CFDs) and the discrete element method (DEM) gas–solid coupling numerical simulation and experiment. The numerical simulation results showed that with the growth of time, the higher the spindle speed, the faster the bonds broke in each part, and the higher the grinding efficiency. When the energy loss of the hammer component was in the range of 985.6~1312.2 J, and the total collision force of the corn straw was greater than 47,032.5 N, the straw grinding effect was better, and the per kW·h yield was higher. The experimental results showed that the optimum combination of operating parameters was a spindle speed of 2625 r/min, a hammer-screen gap of 14 mm, and a sieve pore diameter of 8 mm. Finally, the CFD–DEM gas–solid coupling numerical simulation validation tests were performed based on the optimal combination of the operating parameters. The results showed that the energy loss of the hammer component was 1189.5 J, and the total collision force of the corn straw was 49,523.5 N, both of which were within the range of better results in terms of numerical simulation. Thus, the CFD–DEM gas–solid coupling numerical simulation could accurately predict the corn straw grinding process. This study provides a theoretical basis for improving a hammer mill’s key components and grinding performance. Meanwhile, the proposed gas–solid two-phase flow method provided theoretical references for other research in agricultural machinery.

PH value is a crucial indicator for evaluating silage quality. In this study, taking maize silage as the research object, a quantitative prediction model of pH value change during the secondary fermentation of maize silage was constructed based on computer vision. Firstly, maize silage samples were collected for image acquisition and pH value determination during intermittent and always-aerobic exposure. Secondly, after preprocessing the acquired image with the region of interest (ROI) interception, smoothing, and sharpening, the color and texture features were extracted. In addition, Pearson correlation analysis and RF importance ranking were used to choose useful feature variables. Finally, based on all feature variables and useful feature variables, four regression models were constructed and compared using random forest regression (RFR) and support vector regression (SVR): RFR model 1, RFR model 2, SVR model 1, and SVR model 2. The results showed that—compared with texture features—the correlation between color features and pH value was higher, which could better reflect the dynamic changes in pH value. All four models were highly predictive. The RFR model represented the quantitative analysis relationship between image information and pH value better than the SVR model. RFR model 2 was efficient and accurate, and was the best model for pH prediction, with $R_c^2$, $R_p^2$, RMSEC, RMSEP, and RPD of 0.9891, 0.9425, 0.1758, 0.3651, and 4.2367, respectively. Overall, this study proved the feasibility of using computer vision technology to quantitatively predict pH value during the secondary fermentation of maize silage and provided new insights for monitoring the quality of maize silage.

Larger vibration and noise often exist in agricultural machinery due to the harsh working environment and high power. The rubbing machine is one of the indispensable pieces of equipment in the agriculture and livestock industry, and it is affected by the vibration of large constraints on its promotion and use. To reduce the vibration of the rubbing machine, the vibration characteristics of the spindle rotor were first analysed by modal simulation, thus determining the larger contributions to the spindle rotor vibration. Second, aluminium foam material was installed in the large deformation part of the spindle rotor. Its vibration reduction and energy absorption characteristics were used to optimise the vibration reduction design by increasing the damping. Third, a steel ball impact test was conducted to analyse the vibration characteristics of the optimised spindle rotor. The results show that the maximum impact accelerations were reduced by 28.4% and 64.75% in the axial and radial directions, respectively, and the impact energies were reduced by 67.3% and 90.65% in the axial and radial directions within 2 s of impact collision, respectively, indicating that the optimised spindle rotor damping increased significantly. In addition, the vibration reduction effect of the optimised rubbing machine was verified by a bench test. By measuring the change degree of the static component of the spindle rotor vibration, the axial, radial, and vertical vibrations of the spindle rotor were improved by 5.78%, 10.32%, and 23.96%, respectively. Therefore, optimising the spindle rotor with aluminium foam material can reduce the vibration generated during the impact of the material on the spindle rotor. The rubbing machine’s vibration, damping, and energy absorption were also realised in real working conditions.

Mycotoxin contamination by aflatoxin B₁ (AFB₁) and deoxynivalenol (DON) in maize silage threatens feed safety, requiring rapid, non-destructive monitoring tools. This study developed a visible-light machine vision approach combined with machine learning to quantify AFB₁ and DON and classify contamination levels. A total of 210 silage samples were imaged, and 111 RGB-based color and texture features were extracted, followed by correlation analysis and model-based feature selection. Using the 10 selected features, support vector regression (SVR) achieved the best quantitative performance for AFB₁ (R² = 0.9945, RMSE = 3.18 µg·kg⁻¹), while XGBoost performed best for DON (R² = 0.9816, RMSE = 45.50 µg·kg⁻¹). For classification, random forest and XGBoost correctly identified AFB₁ contamination levels with an Accuracy of 90.48%, whereas SVM achieved 97.62% Accuracy for DON. Comparison of correlation-based and model-based feature importance confirmed the complementary value of statistically significant and nonlinearly predictive features. Biological interpretation suggested that color and texture responses may reflect fungal pigmentation/browning and multiscale surface-structure alterations, respectively, with selected texture descriptors changing earlier than color descriptors during aerobic exposure. The proposed low-cost RGB imaging strategy, coupled with multi-feature fusion, offers a promising approach for high-throughput preliminary mycotoxin screening in feed production.