Moisture content is an important indicator that affects green tea processing. In this study, taking Chuyeqi tea as the research object, a quantitative prediction model of the changes in moisture content during the processing of green tea was constructed based on machine vision and near-infrared spectroscopy technology. First, collect the spectrum and image information in the process of spreading, fixation, first-drying, carding, and second-drying. The competitive adaptive reweighted sampling (CARS) method is then used to extract the characteristic wavelengths in the spectrum, and the image's 9 color features and 6 texture features are combined to establish linear PLSR and nonlinear SVR prediction models by fusing the data information from the two sensors. The results show that, when compared to single data, the PLSR and SVR models based on low-level data fusion do not effectively improve the model's prediction accuracy, but rather produce poor prediction results. In contrast, the PLSR and SVR models established by middle-level data fusion have improved the prediction accuracy of moisture content in green tea processing. Among them, the established SVR model has the best effect. The correlation coefficient of the calibration set (Rc) and the root mean square error of calibration (RMSEC) are 0.9804 and 0.0425, respectively, the correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) are 0.9777 and 0.0490 respectively, and the relative percent deviation is 4.5002. The results show that the middle data fusion based on machine vision and near-infrared spectroscopy technology can effectively predict the moisture content in the processing of green tea, which has important guiding significance for overcoming the low prediction accuracy of a single sensor.
Keywords: Data fusion; Green tea processing; Machine vision; Moisture content; Near-infrared spectroscopy.
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