Early detection of different types of crop stress under greenhouse cultivations is critical in order to optimize yield and resource use efficiency. Objective of the present work is to develop a system which, based on remote sensing, will recognize plant stress by combining microclimate and crop physiology data. The innovation of the platform is based on the integration of a remote PRI sensor that is used to correlate PRI measurements and photosynthesis rate (Ps). In this work, the methodology used for the PRI sensor calibration and acquisition is presented. The values recorded by means of the PRI sensor were correlated with the Ps rate obtained with handheld photosynthesis system. Data of PRI and Ps values were collected under different lighting, temperature and plant water status conditions of a greenhouse tomato crop. The basic statistical parameters of mean and standard deviation values are used to estimate spectral correlation at 530 nm and 570 nm on the interested leaf area. The determination coefficient (R²) of the linear regression obtained between the PRI and Ps data was about 0.9. The obtained equation will be integrated in the sensing system and the data will be used to train a machine learning model in order to detect different type of crop stress under greenhouse conditions. This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ).