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.

This study was conducted to identify the key volatile compounds that characterize whether silage has deteriorated and to investigate the intrinsic link between the key compounds and silage odor. First, silages with different aerobic exposure durations were sampled, and sensory evaluation integrating aerobic stability monitoring was used to distinguish whether the silage had deteriorated. Subsequently, headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) was utilized to determine the relative content of the compounds. Using the relative content of the compounds [...] ABSTRACT This study was conducted to identify the key volatile compounds that characterize whether silage has deteriorated and to investigate the intrinsic link between the key compounds and silage odor. First, silages with different aerobic exposure durations were sampled, and sensory evaluation integrating aerobic stability monitoring was used to distinguish whether the silage had deteriorated. Subsequently, headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) was utilized to determine the relative content of the compounds. Using the relative content of the compounds

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.