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.

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index (VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model (HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that (1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD. (2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively. (3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB, LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.