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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xianguo Ren & Haiqing Tian & Kai Zhao & Dapeng Li & Ziqing Xiao & Yang Yu & Fei Liu, 2022. "Research on pH Value Detection Method during Maize Silage Secondary Fermentation Based on Computer Vision," Agriculture, MDPI, vol. 12(10), pages 1-17, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1623-:d:934769
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    1. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    2. Anil Bhujel & Na-Eun Kim & Elanchezhian Arulmozhi & Jayanta Kumar Basak & Hyeon-Tae Kim, 2022. "A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
    3. Peng Xu & Qian Tan & Yunpeng Zhang & Xiantao Zha & Songmei Yang & Ranbing Yang, 2022. "Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning," Agriculture, MDPI, vol. 12(2), pages 1-16, February.
    4. Goldstein Benjamin A & Polley Eric C & Briggs Farren B. S., 2011. "Random Forests for Genetic Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-34, July.
    5. Anita Konieczna & Kamil Roman & Kinga Borek & Emilia Grzegorzewska, 2021. "GHG and NH 3 Emissions vs. Energy Efficiency of Maize Production Technology: Evidence from Polish Farms; a Further Study," Energies, MDPI, vol. 14(17), pages 1-16, September.
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