Microsoft AI for Earth gives grant to two Basque Country projects, one led by NEIKER, to improve the environment
- Both initiatives will receive a grant including training, technical advice and support from Microsoft experts and 12,000 euros to use on the Microsoft Azure cloud platform.
- Led by NEIKER, Predicoa will develop a predictive model to adjust the nitrogen fertiliser doses needed to grow wheat, thus allowing for maximum field yield without harming the environment.
- The Green Cyber Rating project aims to measure corporate cybersecurity inefficiencies and their theoretical and real impact on the environment.
With its AI for Earth programme, Microsoft supports projects seeking solutions to take on the challenges facing society when it comes to improving the environment, fighting climate change and caring for the planet.
In collaboration with the Basque government, the company held a meeting with various professionals and multi-disciplinary working groups so that they could present their proposals and ideas for improving the environment with artificial intelligence. Of the innovative projects presented in these meetings, two are already in development thanks to Microsoft’s AI for Earth programme.
Now, in collaboration with the Basque Government, two more projects were announced to have been added to the initiative. They have received a grant with training, technical advice and support from Microsoft experts and 12,000 euros to use on Microsoft Azure, the cloud computing platform. With the incorporation of Predicoa and Green Cyber Rating, there are now four projects in the Basque Country included in this global initiative to improve the environment with technology and the tools artificial intelligence makes available to workers in a wide range of professions to maximise and expedite the results of their work.
Caring for the planet from the agricultural industry
The first grant-winning project, Predicoa, focusses on the agricultural industry, which is highly significant in the business community of the Basque Country. The initiative aims to develop an AI-based predictive model that allows for early detection of wheat crop yield at the plot level to adjust the amount of nitrogen fertiliser to the crop’s needs. The optimal amount of fertiliser will get the maximum crop yield on each plot without harming the environment.
Said project will have a pilot phase in the Basque Country until next summer, using a number of ancillary data sources and leveraging all the power of the Microsoft Azure cloud to develop a robust predictive model.
“The application of AI to adjust nitrogen fertiliser amounts is a great opportunity to develop sustainable farming systems,” ensured Ana Aizpurua, a senior researcher for NEIKER-Basque Institute for Agricultural Research and Development. “The initial phase of identifying the variables involved in the harvest is paramount to our project. Defining the necessary Microsoft Azure tools, as well as properly configuring them and implementing the workflow will allow us to make the most of the cloud capacity and extrapolate the model to other farming conditions.”
In addition to Ana Aizpurua, the project also boasts collaboration from Xabier Garitano, head of the Geographic Information Systems Area of HAZI Fundazioa; José Luis Fresno, manager, GARLAN., S.COOP; Kayus Almeida, co-founder and CTO, DRONAK; and Jesús Ángel Bravo, partner and tech expert, ARGI VENTURES, S.L.
Security plus climate change with Green Cyber Rating
The second grant-winning project is focussed on the importance of cybersecurity in the environment. Normally, when someone mentions cyber threats, the mind always goes straight to data and financial costs. However, as the digital transformation continues, so grows the risk of attacks on critical systems that can cause uncontrolled spills of toxic materials, production deficiencies and non-optimised CO2 emissions.
The Green Cyber Rating project aims to develop a new universal standard measurement method by applying artificial intelligence techniques to cybersecurity. Using advanced machine learning methods and prescriptive optimisation, each company or industrial player can be segmented and triangulated at a target “cyber-green” level, in terms of its level of protection and cybersecurity. This will make it possible to predict and automatically interpolate potential attacks, and then measure their potential effects in terms of environmental severity and climatic cost.