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

Xianguo Ren, Haiqing Tian (), Kai Zhao, Dapeng Li, Ziqing Xiao, Yang Yu and Fei Liu
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Xianguo Ren: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Haiqing Tian: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Kai Zhao: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Dapeng Li: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Ziqing Xiao: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Yang Yu: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Fei Liu: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Agriculture, 2022, vol. 12, issue 10, 1-17

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 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.

Keywords: maize silage; pH value; secondary fermentation; computer vision; machine learning; regression model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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