Optimizing water resources is a real issue in some geographical area. Temperature have increased by 0,85°C on average between 1880 and 2012 and couldreach 4.8°C by 2100 compared to the period from 1986 to 2005, according to the last IPCC report. As a result, agriculture and viticulture are facing an increasing water scarcity at the same time as a growing demand. This growing pressure leads to the necessity to optimize available water resources without losing neither yield production nor quality.
Vine irrigation has been used for a very long time in the so-called “new world” vineyards (Australia, Argentina, United States (California) and Chile) and is widely practiced there. Its adoption in Mediterranean regions is much more recent and is one of the first adaptations of wine growers to the consequences of climate change (Ojeda and Saurin, 2014).
Irrigation and water stress management of grapevines is essential in arid and semi-arid areas with limited water supplies to maintain both the quality and quantity of the harvest. This has led the scientific community and companies to develop new technologies for irrigation control, allowing to rationalize the inputs according to the needs of the crop.
WineEO project is a step in this direction with the objective of developing an operational irrigation scheduling service for winegrowers. This service, named Wago, is based on a water balance model (named Sa’irr) mixing three data sources: satellite imagery with optical images coming from the Copernicus program (Sentinel-2), in situ data and meteorological data. It provides farmers with irrigation recommendations (when, where and how much water amount apply over the vineyard to optimize water inputs). Two main challenges are identified in this project:
- Adapting the existing water balance model developed by the Cesbio to vine specificities. Indeed, in contrast to annual crops, vines are characterized by a cover sparsity and a large variability of geometry (rows, inter-rows, vegetation height). Sat’irr model has been mostly developed for one dimensional crops such as wheat and maize. To adapt the model to the vine, it is mandatory to take into account the geometry of the crop. Sentinel-2 optical images are used to determine the growth stage of vineyard. Nonetheless, the spatial resolution of Sentinel-2 bands is one of the limitations for their use in precision viticulture due to the intra-variability of the plots. Advances in Deep Learning in the field of Computer Vision allows enhancing the spatial resolution of these images by using single image super-resolution (SISR) techniques. In the WineEO project, a deep learning SISR was developed to recover a super-resolved Sentinel-2 image at 2.5m in the visible and near-infrared part of the spectrum from its low resolution counterpart.
- Developing a user friendly platform to allow winegrowers to access Wago products. Wago is a decision-making tool developed to help farmers manage their irrigations by providing irrigation recommandations. The tool is based on Sat’irr model and optical images and calculates the water balance on a daily-basis.
The project is led by TerraNIS, which is in charge of the industrialization and commercialization of the service. The Cesbio, a French laboratory, will adapt the Sat’irr water balance model embedded in Wago. Finally, four end-users are targeted in four different countries – Portugal, Italy, Spain and Chile – to test the application in different agronomic conditions (soil, climate, agricultural practices, etc.).