Timely and accurate monitoring of crop physiology and performance are crucial for decision making in precision agriculture and plant stress phenotyping. Conventional assessment of grapevine physiology and RNAseq sampling are labor-intensive, destructive, unsuitable for automation, subject to measurement and sampling errors, and the instrumentation required can be prohibitive in terms of cost. Therefore, to account for circadian or diurnal effects caused by sampling time, it is critical to developing faster and accurate methodologies to estimate key physiological parameters. We used field-based hyperspectral imaging and PLSR to estimate early indicators of grapevine physiological indicators and analyze identified significant spectral regions for fast and accurate plant health monitoring. The hyperspectral imaging and physiological measurements were carried out at two commercial vineyards in California, USA. The PLSR models were developed between reflectance spectra extracted from hyperspectral images and four vine physiological parameters, including stomatal conductance (Gs) photosynthetic CO2 rate (A), intercellular CO2 concentration (Ci) and transpiration rate (E). The results showed the capability of PLSR models to predict physiological parameters (R2 ≥ 0.6), and the best model was found for Gs (R2 = 0.7). Our work demonstrated the potential of the lightweight and relatively inexpensive hyperspectral camera as an alternative for tedious and high-cost traditional methods. The identified significant spectral regions overlap with the most commonly used remote sensing stress indicator, suggesting that hyperspectral imaging coupled with PLSR has great potential for upscaling and broader agricultural applications.