Research on pH Value Detection Method during Maize Silage Secondary Fermentation Based on Computer Vision

Abstract

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, Rc2, Rp2, 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.

Evaluation of preharvest and postharvest factors on forage crop quality, physiology, and ensiling characteristics

“…The decrease in NDF, ADF and WSC content was due to the decomposition of some cellulose and hemicellulose during silage fermentation, which were consumed as substrates together with WSC by microorganisms (Dean et al, 2005). The pH value serves as a crucial indicator of silage quality, providing insights into the preservation status and the degree of decomposition caused by undesirable microorganisms (Ren et al, 2022). In the present study, the pH level of pre-heading oat silage was significantly lower than that of post-heading oat silage (4.73 vs 5.57), which may be due to the high content of unfermentable components such as NDF and ADF in oats at the post-heading stage, which in turn affects the fermentation of oats by microorganisms.…”

Microbiomics and volatile metabolomics-based investigation of changes in quality and flavor of oat (Avena sativa L.) silage at different stages

“…The decrease in NDF, ADF and WSC content was due to the decomposition of some cellulose and hemicellulose during silage fermentation, which were consumed as substrates together with WSC by microorganisms (Dean et al, 2005). The pH value serves as a crucial indicator of silage quality, providing insights into the preservation status and the degree of decomposition caused by undesirable microorganisms (Ren et al, 2022). In the present study, the pH level of pre-heading oat silage was significantly lower than that of post-heading oat silage (4.73 vs 5.57), which may be due to the high content of unfermentable components such as NDF and ADF in oats at the post-heading stage, which in turn affects the fermentation of oats by microorganisms.…”