TRansition to agriculture 4.0: increasing crop resiliency with Artificial Intelligence Technology
Sector: Water Supply and Storage • Location: Germany
Source: EU Funding & Tenders Portal
Global food security challenges, due to exposure to more frequent and intense climate extremes, are threatening to erode and reverse gains made in ending hunger and malnutrition globally, and to shake the foundations of crop production locally. Therefore, plant breeders are under pressure to tackle climate resiliency and resource efficiency. Machine learning-based technologies have the potential t
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | ended |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
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Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | Global food security challenges, due to exposure to more frequent and intense climate extremes, are threatening to erode and reverse gains made in ending hunger and malnutrition globally, and to shake the foundations of crop production locally. Therefore, plant breeders are under pressure to tackle climate resiliency and resource efficiency. Machine learning-based technologies have the potential to address these challenges and enable breeders to produce stable, value-added products, which contribute to resource-efficient agriculture that can feed the world, but are so far still under development and not commercially available. Computomics has developed xSeedScore, the first disruptive machine learning-based technology enabling more climate-resilient varieties, decreased land and water use, reduced time-to-market and a competitive alternative to genetic modification. With help of the EIC Accelerator, Computomics expects to reach by 2026 a turnover of €18M and 50 employees. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
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Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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