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Using RGB Imaging, Optimized Three-Band Spectral Indices, and a Decision Tree Model to Assess Orange Fruit Quality

Author

Listed:
  • Hoda Galal

    (Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia 32897, Egypt)

  • Salah Elsayed

    (Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia 32897, Egypt)

  • Osama Elsherbiny

    (Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt)

  • Aida Allam

    (Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia 32897, Egypt)

  • Mohamed Farouk

    (Agricultural Engineering, Surveying of Natural Resources in Environmental Systems Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia 32897, Egypt)

Abstract

Point samples and laboratory testing have historically been used to evaluate fruit quality criteria. Although this method is precise, it is slow, expensive, and destructive, making it unsuitable for large-scale monitoring of these parameters. The main objective of this research was to develop a non-invasive protocol by combining color RGB indices (CIs) and previously published and newly developed three-band spectral reflectance indices (SRIs) with a decision tree (DT) model to evaluate the fruit quality parameters of navel orange. These parameters were brightness (L*), red–green (a*), blue–yellow (b*), chlorophyll meter (Chlm), total soluble solids (TSS), and TSS/acid ratio. The characteristics of fruit quality of navel orange samples were measured at various stages of ripening. The outcomes demonstrated that at various levels of ripening, the fruit quality parameters, RGB imaging indices, and published and newly developed three-band SRIs differed. The newly developed three-band SRIs based on the wavelengths of blue, green, red, red-edge, and NIR are most effective for estimating the six measured parameters in this study. For example, NDI 574,592,724 , NDI 572,584,724 , and NDI 574,722,590 had the largest R 2 value (0.90) with L*, whereas NDI 526,664,700 and NDI 524,700,664 exhibited the highest R 2 value (0.97) with a*. Moreover, integrating CIs and SRIs with the DT model has provided a potentially useful tool for the accurate measurement of the six studied parameters. For instance, the DT-SRIs-CIs-30 model performed better in terms of measuring a* using 30 various indices. The R 2 value was 0.98 and RMSE = 1.121 in the cross-validation, while R 2 value was 0.964 and RMSE = 2.604 in the test set. Otherwise, based on the fusion of five various indices, the DT-SRIs-CIs-5 model was the most precise for recognizing b* (R 2 = 0.957 and 0.929, with RMSE = 1.713 and 3.309 for cross-validation and test set, respectively). Overall, this work proves that integrating the different characteristics of proximal reflectance sensing systems such as color RGB indices and SRIs via the DT model may be considered a reliable instrument for evaluating the quality of different fruits.

Suggested Citation

  • Hoda Galal & Salah Elsayed & Osama Elsherbiny & Aida Allam & Mohamed Farouk, 2022. "Using RGB Imaging, Optimized Three-Band Spectral Indices, and a Decision Tree Model to Assess Orange Fruit Quality," Agriculture, MDPI, vol. 12(10), pages 1-19, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1558-:d:926415
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    References listed on IDEAS

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    1. Osama Elsherbiny & Yangyang Fan & Lei Zhou & Zhengjun Qiu, 2021. "Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data," Agriculture, MDPI, vol. 11(1), pages 1-21, January.
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    1. Nicola Cinosi & Silvia Portarena & Leen Almadi & Annalisa Berrettini & Mariela Torres & Pierluigi Pierantozzi & Fabiola Villa & Andrea Galletti & Franco Famiani & Daniela Farinelli, 2023. "Use of Portable Devices and an Innovative and Non-Destructive Index for In-Field Monitoring of Olive Fruit Ripeness," Agriculture, MDPI, vol. 13(1), pages 1-13, January.

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