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Construction of a Chlorophyll Content Prediction Model for Predicting Chlorophyll Content in the Pericarp of Korla Fragrant Pears during the Storage Period

Author

Listed:
  • Yang Liu

    (College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Jinfei Zhao

    (College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Yurong Tang

    (College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Xin Jiang

    (College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

  • Jiean Liao

    (College of Mechanical Electrification Engineering, Tarim University, Alaer 843300, China
    Agricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China)

Abstract

A chlorophyll content prediction model for predicting chlorophyll content in the pericarp of Korla fragrant pears was constructed based on harvest maturity and storage time. This model predicts chlorophyll content in the pericarp of fragrant pears after storage by using the error backpropagation neural network (BPNN), generalized regression neural network (GRNN) and adaptive neural fuzzy inference system (ANFIS). The results demonstrate that chlorophyll content in the pericarp of fragrant pears decreased gradually as the harvest time lengthened. The chlorophyll content in the pericarp of fragrant pears with different maturity levels at harvest decreased continuously with the increase in storage time. According to a comparison of the prediction performances of the BPNN and ANFIS models, it was discovered that the trained GRNN and ANFIS models could predict chlorophyll content in the pericarp of fragrant pears. The ANFIS model showed the best prediction performances when the input membership functions were gasuss2mf (RMSE = 0.006; R 2 = 0.993), dsigmf (RMSE = 0.007; R 2 = 0.992) and psigmf (RMSE = 0.007; R 2 = 0.992). The findings of this study can serve as references for determining the delivery quality and timing of Korla fragrant pears.

Suggested Citation

  • Yang Liu & Jinfei Zhao & Yurong Tang & Xin Jiang & Jiean Liao, 2022. "Construction of a Chlorophyll Content Prediction Model for Predicting Chlorophyll Content in the Pericarp of Korla Fragrant Pears during the Storage Period," Agriculture, MDPI, vol. 12(9), pages 1-12, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1348-:d:903283
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    References listed on IDEAS

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    1. Yuanyuan Liu & Tongzhao Wang & Rong Su & Can Hu & Fei Chen & Junhu Cheng, 2021. "Quantitative Evaluation of Color, Firmness, and Soluble Solid Content of Korla Fragrant Pears via IRIV and LS-SVM," Agriculture, MDPI, vol. 11(8), pages 1-16, July.
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    Cited by:

    1. Elham Alzain & Shaha Al-Otaibi & Theyazn H. H. Aldhyani & Ali Saleh Alshebami & Mohammed Amin Almaiah & Mukti E. Jadhav, 2023. "Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.

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