The project aims to develop a non-invasive hybrid sensing system that combines near-infrared and Raman spectroscopy with machine learning models to monitor key chemical substances in grapes, such as sugar, acidity, and anthocyanins. The system will be experimentally validated at the CSU Fresno vineyard using samples from 70 different varieties of table and wine grapes. This technology could enable ultra-precision agricultural practices and be integrated into future robotic data scouts for large-scale mapping of grape quality in real-time.
Non-invasive Hybrid Sensing of Key Chemical Compositions of Grapes
