Variable rate irrigation scheduling based on high-resolution short-wave infrared sensing and internet-of-things.

Summary:  Vineyards face climate change, increasing temperatures, and drought affecting vine water status. Water deficit affects plant physiology and can ultimately decrease yield and grape quality when it is not well managed. Monitoring vine water status and irrigation can help growers better manage their vineyards. However, when field measurements, such as stem water potentials (SWP), can be precise, they are time-consuming. In addition, they do not allow for easy assessment of spatial variability, which is a critical factor for water status management. Remote sensing tools can help map plant water status in space and time and streamline data acquisition over whole vineyards several times during the season. In this project, we monitored a variably irrigated vineyard several times during the season with a hyperspectral NIR/SWIR camera mounted on a UAV.

A study was put in place in a Vitis vinifera L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) – Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The stem was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting stem values with an R2 of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation.

As the drought conditions become more familiar to California grapevine growers, the need for effective water management solutions has heightened. Understanding the physiological responses of the plant at various intensities and timings can still provide great context for more advanced developments.