Gamaya will participate in food security conference in Brazil

Gamaya, provider of the world’s most advanced solution for diagnostics of farmland using hyperspectral imaging and artificial intelligence, will open the Food Security Conference, the event organized by House of Switzerland and dedicated to answer the question – what is needed to successfully face the challenge of food security? Apart from food security experts the Food Security Conference also mobilizes the startup and innovation ecosystem from both Brazil and Switzerland in order to leverage innovative solutions such as new ways of eating and how to produce food efficiently. The conference will take place at the House of Switzerland in Rio de Janeiro on 15th of September 2016. You can register here.

We are going through a moment when we have to rethink the priorities of humanity. By 2050 the world population will reach the 9 billion, and this raises the question of how to feed the entire planet and increase food productivity by 70% over the next 30 years. What is needed to successfully face the challenge of food security?

Our CEO, Yosef Akhtman, will be among speakers of the Panel called “The role of innovation in sustainable food production”. Yosef will open the Food Security Conference with an overview of global challenges of food security.

Interview with Yosef Akthman, CEO of Gamaya, at the Food Security conference organized by the House of Switzerland in Brazil

Gamaya provides the world’s most advanced solution for diagnostics of farmland using a unique constellation of patented hyperspectral imaging technology, drone-based deployment and artificial intelligence. Gamaya farmland analytics solution improves production efficiency and risk management by facilitating optimised usage of chemicals and fertilisers, as well as reducing disease and weed-related losses. Some of the information services that we provide include the detection and diagnostics of crop disease, classification of weeds, analysis of soil, optimization of fertilization, as well as prediction of yield.