Location: The project located the farms that were in the study, in the area of Castejón, a town in Navarra, where conditions were unbeatable, ensuring the fairness of the soil conditions of the farms and their crops.

Work performed: During the year and a half that the project has lasted, all the equipment operators have monitored all the data collected, which were subsequently included and analyzed by the irrigation recommendation platform, which will allow automation by the platform's algorithms. The data collected are:

Visual data:
- Physically by customer's operators and installers.
- Periodic drone flights to implement them in the platform.
- All this monitoring facilitated the creation of a virtual twin of the farms on the platform.

Thermal data:
- A thermal drone performed flights on a regular basis, to align with Sentinel 2A and Sentinel 2B satellite overpasses.
- The drone flights were conducted roughly 3 hours from the Sentinel 2 passes to increase the quality of the classification of the drone data with the satellite data.

Field Data:
- Ambient temperature and relative humidity data were collected using Spherag's Atlas devices. The Atlas devices, incorporating the necessary high quality sensors, collected these data accurately throughout the duration of the project and, given their real-time connectivity, stored the historical data in the platform, allowing it to be accessible by all team members at all times.
- The Atlas equipment was installed and serviced throughout the project by RIS Iberia and Spherag personnel.

Hyperspectral data:
- HYP data were taken by the PRISMA satellite, which had been previously tested in the first annuity.

Instead of irrigating the entire farm all at once, a smart strategy has been implemented: dividing irrigation into plots. Each area receives water according to its specific needs. Automated RIS Valves excel in this precise and efficient management. Farmers can be confident that each almond tree receives the right amount of water without wasting resources.

The project carried out by the consortium progressed significantly despite some technical and temporal challenges. Data collection and analysis, together with the installation of monitoring equipment, provided valuable information on plot conditions and crop yields.

The recommendation models have been developed meeting the established milestones and all the initially projected results/objectives have been achieved. Among all the milestones, we would like to highlight the integration of all the data, based on local sensors, NDVI data from satellite images, and TIR and HYP images, and the development and integration of both recommendation models in a single platform accessible in real time from any device with internet connection from any point of the planet.

We can say with some confidence that a good correlation is derived between LST and crop health/yield. The images shown above show a time series, where it can be seen that the areas with higher temperatures correspond to the areas with less green areas.

Traditionally, the agricultural sector over-irrigates crops. A good use of hyperspectral imagery will result in very significant savings in water and fertilizer consumption.

The information that we can give the companies of the consortium to the companies and self-employed in the agricultural sector opens new lines of business, thanks to the irrigation recommendations provided by platforms such as the company SPHERAG IoT.

In conclusion we say that we are able to make an effective irrigation recommendation, thanks to SPHERAG devices, CONSTELLR and GPZ analysis of TIR and HYP images and the efficient creation of the recommendation platform that end users can have in the palm of their hand.