A quantitative prediction model for the moisture content of work-in-process Yongchuan Xiuya tea was established using partial least squares regression (PLSR) based on its color changes as evaluated using different color models. The results showed that during the initial production process, the red-green and mean blue channel value increased, while the moisture content and 15 other color model components such as lightness, yellow-blue, mean red channel value, mean green channel value and mean hue value decreased, suggesting that the color became darker and yellower. Through heatmap and cluster analysis, the samples were divided into two categories and four sub-categories, and the carding process had the most significant impact on the moisture and color of the products. Based on the 17 color model components, the predictive model was established, and its performance was evaluated by considering correlation coefficient of calibration set (R_c), root-mean-square error of cross-validation (RMSECV), correlation coefficient of predication set (R_p), root-mean-square error of prediction (RMSEP) and relative percent deviation (RPD). The values of R_c, R_p, RMSECV and RMSEP were 0.979, 0.980, 0.0447, and 0.0443, respectively. The difference between RMSECV and RMSEP was merely 0.0004, and the RPD value was 5.04, indicating that the model had excellent prediction capacity and generalization capacity and could provide a new method for the online monitoring of the moisture content in work-in-process Yongchuan Xiuya tea.

The tea plant is a valuable and evergreen crop that is extensively cultivated in China and many other countries. Currently, there is growing research interest in this plant. For the tea industry, it is crucial to develop rapid and non-invasive methods to evaluate tea plants in their natural environment. This article provides a comprehensive overview of non-invasive sensing techniques used for in-situ detection of tea plants. The topics covered include leaf, canopy, and field-level assessments, as well as statistical analysis techniques and characteristics specific to the research. Non-invasive testing technology is primarily used for monitoring and predicting tea pests and diseases, monitoring quality, and nutrients, determining tenderness and grade, identifying tea plant varieties, automatically detecting, and identifying tea buds, monitoring tea plant growth, and extracting tea garden areas through remote sensing. It also helps to evaluate planting suitability, assess disasters, and estimate yields. Additionally, the article examines the challenges and prospects of emerging techniques aimed at resolving the in-situ detection problem for tea plants. It can assist researchers and producers in comprehensively understanding the tea environment, quality characteristics, and growth process, thereby enhancing tea production quality, and fostering tea industry development.