دانلود مقاله انگلیسی رایگان:پیش بینی دقیق محتوای جامد محلول سیب از مناطق مختلف جغرافیایی با ترکیب یادگیری عمیق با ویژگی های اثر انگشت طیفی - 2019
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  • Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features
    Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features


    ترجمه فارسی عنوان مقاله:

    پیش بینی دقیق محتوای جامد محلول سیب از مناطق مختلف جغرافیایی با ترکیب یادگیری عمیق با ویژگی های اثر انگشت طیفی


    منبع:

    Sciencedirect - Elsevier - Postharvest Biology and Technology, 156 (2019) 110943: doi:10:1016/j:postharvbio:2019:110943


    نویسنده:

    Yuhao Bai, Yingjun Xiong, Jichao Huang, Jun Zhou, Baohua Zhang


    چکیده انگلیسی:

    The geographical origin of an apple can affect its cellular structure, and therefore its optical properties including interactions with incident light. As a result, accurate prediction of soluble solid content (SSC) in apples with multiple geographical origins is still challenging. A multiple-origin SSC prediction model for apples from multiple geographical regions has been developed by combining spectral fingerprint feature extraction, origin recognition, model search strategies, optimal wavelength selection, and deep learning with multivariate regression analysis. In this model, the spectral fingerprint features of apples were explored and determined using the random frog algorithm, and deep learning was used to train and test for origin recognition with the fingerprint spectral feature as inputs. Particle least squares (PLS) was applied to develop individual-origin calibration models, and subsequently employed to detect SSCs. A competitive adaptive reweighted sampling (CARS) algorithm was used to select the optimal wavelengths for the calibration models. Compared with the individualorigin model, the proposed multiple-origin model achieved more accurate results for the prediction of SSC of apples with multiple geographical origins, with the RP and RMSEP values being 0.990 and 0.274, respectively. These results indicate that variations in geographical origin affect accuracy, but that the multiple-origin model can eliminate the effects of geographical origin on SSC prediction, thereby improving the applicability of SSC detection in practice.
    Keywords: Near-infrared spectroscopy | Spectral fingerprint features | Multiple-origin model | Soluble solid content | Deep learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 9
    حجم فایل: 2089 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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