首页|Sichuan Agriculture University Researcher Broadens Understanding of Machine Lear ning (Application of Hyperspectral Technology with Machine Learning for Brix Det ection of Pastry Pears)
Sichuan Agriculture University Researcher Broadens Understanding of Machine Lear ning (Application of Hyperspectral Technology with Machine Learning for Brix Det ection of Pastry Pears)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news reporting originating from Sichuan Agriculture Uni versity by NewsRx correspondents, research stated, “Sugar content is an essentia l indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction.” Our news reporters obtained a quote from the research from Sichuan Agriculture U niversity: “In this study, we paired a support vector machine (SVM) algorithm wi th genetic algorithm optimization to reliably estimate the sugar content in cris p pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and con volution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed wi th convolution smoothing (SG) was the most accurate, with a correlation coeffici ent 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algor ithm (SPA) to select key representative wavelengths from the spectral data. Fina lly, we used the retrieved characteristic wavelength data to create a support ve ctor machine model (GASVR) that was genetically tuned.”
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