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Smart Agriculture

Artificial Intelligence can flank human intelligence in the agriculture area. In this infograph some data and applications telling how much AI is relevant to agriculture today. Source: Cognilytica

Italy, as most Countries in the world, used to be a country of farmers and farmers were at the same time considered “dense” and “field-smart”. We even have a saying in Italian about the “intelligenza contadina” -farmer intelligence- meaning a kind of intuitive grasp of reality, been smart on concrete things.

Indeed, farming has been an exercise in data analytics well before we had an idea of what data analytic is about. Farming means understanding the soil and its changes over time, make educated guesses on weather (wind, rain, sunlight…) watch out for pests and a sense of timing… Couple this with a sense of economics, market evolution, availability of seeds, fertilisers, tools and you get avery complex environment. No wonder that sometimes the yields were unsatisfactory.

Experience was the guiding beacon, but even that failed sometime.

The industrial revolution drained farms from workers enrolling them in factories but that would not have been possible if at the same time new tools could dramatically change the workforce need in agriculture. Nowadays agriculture productivity is hundred times more than 2000 years ago (which, by the way, remained the same till the industrial revolution in Europe). In the last 50 years productivity has multiplied by 5 times, thanks to tools, fertilisers and seed selection.

These successes are not sufficient to tackle the growth in population worldwide and innovation seems to be the way to further improve productivity.

Artificial Intelligence, particularly data analytics harvesting the huge amount of data (and data streams), seems to be a must in this search for increased productivity.  As shown in the figure, farms are incredible generators of data points, thanks to pervasive use of IoT, including aerial imagining taken from drones. Other streams of data come from weather forecast, biological monitoring (pests, seeds type and so on). The information emerging from data analytics are used in planning, in early detection of plant diseases, monitoring of weeds resulting in higher yield and less use of pesticides and herbicides.  As the number of farmers shrinks, robotisation increases and that has been the trend for the last two decades. What could have been robotised easily has been robotised. Further penetration calls for smarter robots that can understand the environment and adapt their behaviour. This is where AI comes in.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.