Fact finding on Gamaya
Business and mission
Gamaya improves efficiency and sustainability of farming businesses by offering compelling agronomy solutions, enabled by hyperspectral imaging and artificial intelligence
The company uses drones equipped with hyperspectral cameras that can capture changes in water and fertilizer use, crop yields, and pests. It then analyzes those images with AI algorithms to alert farmers to potential problems and predict outcomes based on patterns. Gamaya says these alerts and predictions will help farmers lower costs for water and fertilizer while improving their yields
Gamaya was spun out of the Swiss Federal Institute of Technology, a research university in Switzerland, and received funding from Nestle's Chairman, Peter Brabeck-Letmathe, Swiss venture capital firm VI Partners, and others.
Yosef Akhtman, CEO
Problem they solve
Feeding a planet with nine billion people by 2050 will be a major challenge. But tech companies are increasingly looking to solve the problem using the latest in computing technology.
The human eye can’t see changes in aspects like water and fertilizer use, crop yield, and emergence of pests
Farmers therefore need to use fertilizers and more water to grow more crops.
How they solve it ?
Through the use of drone imagery, the company provides farmers with alerts about pests and disease, yield predictions, and prescriptions for input application rates. Its current platform services corn, soybean, and sugarcane growers. Gamaya differentiates itself from others in the drone imagery space through its hyperspectral sensing technology; the majority of drone imagery companies use multispectral images. Hyperspectral imagery has been available for 20 years, but it’s been an expensive exercise constructing the equipment, hiring developers, and analyzing the data, so has mainly been used by large research institutes, space agencies and the military, according to Igor Ivanov, chief commercial officer of Gamaya.
The company’s main intellectual property is in its analysis of this hyperspectral imagery using artificial intelligence to produce information about the plant physiology.To develop this analytical product for a specific issue, crop and region, Gamaya needs ground samples from each region to input into its algorithm. Early adopters or trial customers are helping to provide these, according to Yosef Akhtman, CEO of Gamaya.
Why is this a company to watch and follow ?
Gamaya differentiates itself from others in the drone imagery space through its Hyperspectral sensing technology; the majority of drone imagery companies use multispectral images.
Hyperspectral imagery has been available for 20 years, but it’s been an expensive exercise.
Gamaya is partnering with the Belgian Nanotechnology Research Institute IMEC to produce affordable hyperspectral sensors based on a design the Gamaya founders developed out of Swiss Federal Technological Institute in Lausanne (EPFL).
Multispectral cameras can measure generic characteristics such as if a plant is healthy or not, but hyperspectral images can go one step further, and diagnose the exact reason for that state.
According to a recent article in Tech.eu that listed 10 European Agtech start ups to watch, food demand is doubling by 2050 so it makes sense that technology should play a role in shaping new agriculture on a global scale.
What will improve their contribution to Inclusive Growth
Gamaya has high-value products and customization is almost inevitable as it makes financial sense to customize a product for a particular market segment that is characterized by similar growing practices, region, climate and soil conditions.
Gamaya focuses on only a few mid and high-value crops and high-impact issues — diseases, weeds, nutrient deficiencies — in consolidated regions.
It will take time and extra funding to build the different versions of the working model before they will reach a bigger market and therefore more farmers.
There is a tremendous untapped potential in agtech products and services to address specific stresses in specific crops in each region.