Gamaya

Last updated
GAMAYA
IndustryFarming technology
Founded2015
FounderDragos Constantin, Igor Ivanov and Yosef Akhtman
Headquarters Morges, Switzerland
Key people
Yury Vasilkov (CEO)
ServicesArtificial Intelligence-powered platforms for sugarcane industry optimization, Remote sensing integration for agronomic insights
Website www.gamaya.com

Gamaya is a Swiss company that initially gained recognition for providing drones equipped with hyper-spectral cameras for use in agriculture. [1] [2] These drones were designed to collect detailed data on crop conditions, enabling precision farming.

Contents

Over time, the company has shifted its focus to the development of Artificial Intelligence (AI)-powered solutions. Today, Gamaya offers a comprehensive platform tailored specifically to the needs of the global sugarcane industry. Leveraging remote sensing and agronomic intelligence, the company creates specialised technology that supports sugarcane mills in optimizing their operations and productivity.

The company has 35 employees and is located in Morges, Switzerland, with an additional office in Brazil.. [3] Yury Vasilkov is the current company CEO. [4]

Committed to sustainability, Gamaya prioritises carbon removal and efficient water management, driving sustainable agricultural practices that are adaptable to climate change. Its solutions enable sugarcane mills to optimise operational efficiency and maximise yields, promoting a strategic value chain.

History

Hyperspectral camera technology

Gamaya's hyperspectral camera technology was developed between 2013 and 2015 through the EPFL Leman-Baikal project. [12] It captures 40 different bands of light. [2] Hyperspectral images shot with a drone are then used to generate a survey of the land. [13] The data can be used for detecting invasive species and crop diseases, predicting yields, or for optimising soil treatment and fertilisation. [9]

Gamaya's cameras have been used in a few scientific studies as part of data collection for hyperspectral survey of agricultural crops. [14] [15] [16]

Products

Related Research Articles

<span class="mw-page-title-main">Precision agriculture</span> Farming management strategy

Precision agriculture (PA) is a management strategy that gathers, processes and analyzes temporal, spatial and individual plant and animal data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.” It is used in both crop and livestock production. Precision agriculture often employs technologies to automate agricultural operations, improving their diagnosis, decision-making or performing. The goal of precision agriculture research is to define a decision support system for whole farm management with the goal of optimizing returns on inputs while preserving resources.

<span class="mw-page-title-main">Agriculture in Thailand</span>

Agriculture in Thailand is highly competitive, diversified and specialized and its exports are very successful internationally. Rice is the country's most important crop, with some 60 percent of Thailand's 13 million farmers growing it on almost half of Thailand's cultivated land. Thailand is a major exporter in the world rice market. Rice exports in 2014 amounted to 1.3 percent of GDP. Agricultural production as a whole accounts for an estimated 9–10.5 percent of Thai GDP. Forty percent of the population work in agriculture-related jobs. The farmland they work was valued at US$2,945/rai in 2013. Most Thai farmers own fewer than eight ha (50 rai) of land.

<span class="mw-page-title-main">Hyperspectral imaging</span> Multi-wavelength imaging method

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. There are three general types of spectral imagers. There are push broom scanners and the related whisk broom scanners, which read images over time, band sequential scanners, which acquire images of an area at different wavelengths, and snapshot hyperspectral imagers, which uses a staring array to generate an image in an instant.

Controlled-environment agriculture (CEA) -- which includes indoor agriculture (IA) and vertical farming—is a technology-based approach toward food production. The aim of CEA is to provide protection from the outdoor elements and maintain optimal growing conditions throughout the development of the crop. Production takes place within an enclosed growing structure such as a greenhouse or plant factory.

<span class="mw-page-title-main">Sugarcane</span> Several species of grass used for sugar production

Sugarcane or sugar cane is a species of tall, perennial grass that is used for sugar production. The plants are 2–6 m (6–20 ft) tall with stout, jointed, fibrous stalks that are rich in sucrose, which accumulates in the stalk internodes. Sugarcanes belong to the grass family, Poaceae, an economically important flowering plant family that includes maize, wheat, rice, and sorghum, and many forage crops. It is native to New Guinea.

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<span class="mw-page-title-main">Sugarcane harvester</span> Harvesting machine

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<span class="mw-page-title-main">Indigo Agriculture</span> American agricultural technology company

Indigo Agriculture is a Boston, Massachusetts-based agricultural technology company that works with plant microbes, aiming to improve yields of cotton, wheat, corn, soybeans, and rice. The company also offers crop storage and other logistics programs for farmers.

<span class="mw-page-title-main">Agricultural drone</span> Unmanned aerial vehicle

An agricultural drone is an unmanned aerial vehicle used in agriculture operations, mostly in yield optimization and in monitoring crop growth and crop production. Agricultural drones provide information on crop growth stages, crop health, and soil variations. Multispectral sensors are used on agricultural drones to image electromagnetic radiation beyond the visible spectrum, including near-infrared and short-wave infrared.

<span class="mw-page-title-main">Digital agriculture</span> Electronic agricultural data collection, storage, analysis, and sharing

Digital agriculture, sometimes known as smart farming or e-agriculture, are tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture. The Food and Agriculture Organization of the United Nations has described the digitalization process of agriculture as the digital agricultural revolution. Other definitions, such as those from the United Nations Project Breakthrough, Cornell University, and Purdue University, also emphasize the role of digital technology in the optimization of food systems.

The sugar industry of the United States produces sugarcane and sugar beets, operates sugar refineries, and produces and markets refined sugars, sugar-sweetened goods, and other products. The United States is among the world's largest sugar producers. Unlike most other sugar producing countries, the United States has both large and well-developed sugarcane and sugar beet industries. Refined sugarcane, processed sugar beet, and high-fructose corn syrup are all commonly used in the U.S. as added sugars to sweeten food and beverages.

<span class="mw-page-title-main">Pixxel</span> Indian aerospace company

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References

  1. Fehrenbacher, Katie (2016-05-23). "This Startup Is Changing Farming With Drones and AI". Fortune. Retrieved 2020-07-01.
  2. 1 2 O'Dea, Clare (2020-04-22). "How Swiss technology is changing farming". SWI swissinfo.ch. Retrieved 2020-07-03.
  3. Thoele, Alexander (2019-07-04). "Une start-up suisse développe des caméras qui «parlent» aux plantes" [A Swiss startup develops cameras which "talk" to plants]. SWI swissinfo.ch (in French). Retrieved 2020-07-03.
  4. "New CEOs for ambitious start-ups". www.startupticker.ch. Retrieved 2021-07-19.
  5. Zaki, Myret (2017-05-05). "Drones et agriculture: Gamaya remporte le Prix Strategis 2017" [Drones and agriculture: Gamaya wins the Strategis Award]. Bilan (in French). Retrieved 2020-07-01.
  6. "Nestle Chairman Commits to $3.2m Series A for Hyperspectral Drone Imagery Company Gamaya". AgFunderNews. 2016-05-25. Retrieved 2020-07-03.
  7. kaczor, Piotr (2017-06-06). "Deux lauréats romands et un zurichois primés". Agefi (in French). Retrieved 2020-07-01.
  8. Zeitung, Jungfrau (2017-06-02). "Die besten Jungunternehmen der Schweiz stehen fest". Jungfrau Zeitung (in German). Retrieved 2020-07-03.
  9. 1 2 Ohr, Thomas (2017-10-11). "Meet Gamaya: The farmland analytics startup that just won the Inception Awards at GTC Europe". EU-Startups. Retrieved 2020-07-03.
  10. Ohr, Thomas (2019-07-19). "Swiss AgTech startup Gamaya raises about €10.9 million Series B funding to expand crop intelligence solutions". EU-Startups. Retrieved 2020-07-03.
  11. Gaur, Vatsala (2019-06-14). "M&M picks up 11.25% in Swiss Agri-tech firm Gamaya". The Economic Times. Retrieved 2020-07-03.
  12. "Leman-Baikal Project". EPFL. Retrieved 2020-07-03.
  13. Kite-Powell, Jennifer (2015-08-24). "Sensors, Insects, Drones And Sustainable Nitrogen Define Innovation In European AgTech Start Ups". Forbes. Retrieved 2020-07-03.
  14. Danilov, Roman; Zelensky, Roman; Ponomarev, Artyom; Ivanisova, Mariya; Gasiyan, Kseniya (2020). "Development of precision methods for remote monitoring of weeds". BIO Web of Conferences. 21: 00003. doi: 10.1051/bioconf/20202100003 . ISSN   2117-4458.
  15. Akhtman, Y.; Golubeva, E.; Tutubalina, O.; Zimin, M. (2017-12-29). "Application of hyperspectral images and ground data for precision farming". Geography, Environment, Sustainability. 10 (4): 117–128. doi: 10.24057/2071-9388-2017-10-4-117-128 . Retrieved 2020-07-01.
  16. Förster, Michael; Schmidt, Tobias; Wolf, Roman; Kleinschmit, Birgit; Fassnacht, Fabian E.; Cabezas, Julián; Kattenborn, Teja (2017-06-01). "Detecting the spread of invasive species in central Chile with a Sentinel-2 time-series". 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). pp. 1–4. doi:10.1109/Multi-Temp.2017.8035216. ISBN   978-1-5386-3327-4. S2CID   3267718.