Scientific assistance for sensor selection, AI modeling and remote monitoring in field trials for water usage optimization in grapevines

Optimising water management in vineyards is of great importance for sustainability. As a finite resource, fresh water use for irrigation must be used only when strictly necessary and in the right amounts. Currently, irrigation volumes in vineyards are calculated over the basis of theoretical water consumption estimations performed using only climatic parameters (e.g. calculating fractions of the reference evapotranspiration). Very often the actual plant water status is neglected, mainly because the difficulties of performing reliable measurements in a representative way for a vineyard. Therefore, finding a good proxy for plant water status is pivotal to optimize water management in crops. Artificial intelligence approaches could represent a tool to improve our prediction capabilities. However, artificial intelligence and machine learning approaches require a large amount of data to calibrate the working algorithms. The aim of the project is to provide possible inputs regarding grapevine water use and water status in a digital and continuous way, with proper standard validation measurements, to be use in a machine learning algorithm, with the ultimate goal of training an artificial intelligence able to decide when and how much to irrigate grapevines while optimising the water resources.