1 results match your criteria: "Research Institute of Pomology of Jingning Country[Affiliation]"
Front Plant Sci
November 2024
Research Institute of Pomology of Jingning Country, Pingliang, China.
This work aims to predict the starch, vitamin C, soluble solids, and titratable acid contents of apple fruits using hyperspectral imaging combined with machine learning approaches. First, a hyperspectral camera by rotating samples was used to obtain hyperspectral images of the apple fruit surface in the spectral range of 380~1018 nm, and its region of interest (ROI) was extracted; then, the optimal preprocessing method was preferred through experimental comparisons; on this basis, genetic algorithms (GA), successive projection algorithms (SPA), and competitive adaptive reweighting adoption algorithms (CARS) were used to extract feature variables; subsequently, multiple machine learning models (support vector regression SVR, principal component regression PCR, partial least squares regression PLSR, and multiple linear regression MLR) were used to model the inversion between hyperspectral images and internal nutrient quality physicochemical indexes of fruits, respectively. Through the comparative analysis of all the model prediction results, it was found that among them, for starch, vitamin C, soluble solids, and titratable acid content, 2 Der-CARS-MLR were the optimal prediction models with superior performance (the prediction coefficients of determination R exceeded 90% in all of them).
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