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.

水分含量是影响绿茶加工的重要指标。本研究以初叶旗茶为研究对象，基于机器视觉和近红外光谱技术构建了绿茶加工过程中水分变化的定量预测模型。首先，收集铺布、固色、一次干燥、梳理、二次干燥过程中的光谱和图像信息。然后采用竞争自适应重加权采样（CARS）方法提取光谱中的特征波长，结合图像的9个颜色特征和6个纹理特征，通过融合两者的数据信息，建立线性PLSR和非线性SVR预测模型传感器。结果表明，与单一数据相比，基于低层数据融合的PLSR和SVR模型并不能有效提高模型的预测精度，反而会产生较差的预测结果。相比之下，中层数据融合建立的PLSR和SVR模型提高了绿茶加工过程中水分含量的预测精度。其中，建立的SVR模型效果最好。校准集的相关系数（Rc）和校准的均方根误差（RMSEC）分别为0.9804和0.0425，预测集的相关系数（Rp）和预测的均方根误差（RMSEP）分别为分别为 0.9777 和 0.0490，相对百分比偏差为 4.5002。