Machine Learning and Artificial Intelligence are speeding up the development of new crop varieties to adapt to climate change — to sustain heat!

Among the UN 17 Sustainable Development Goals (SDGs) is achieving food security and improved nutrition within a sustainability perspective, by the year 2030. These SDGs succeeding the previous UN Millennium Development Goals are all interconnected. Interestingly it is the food that connects them all as per the embedded model by the Stockholm Resilience Center.

Figure 1: This model with embedded SDGs was developed by the Stockholm Resilience Center (Azote Images for Stockholm Resilience Centre — 2017).
Figure 2: Mean Temperature (°C) over time (on left stations and on right maps generated based on kriging techniques) for 4 different years (1995, 1975, 1995, and 2015) — Meteorological Data from Environment Canada
Figure 3: Record-Breaking Climate Trends Briefing — July 19, 2016 — Higher than normal temperatures are shown in red and lower then normal temperatures are shown in blue. Credit: NASA/GISS
Figure 4: Surfaces of temperatures used to search for traits in the samples — Illustration prepared by my friend Dag Endresen with whom and other colleagues from different countries we conducted this ML-based research.
Figure 5: Major genbanks worldwide with safety deposits in the Svalbard Global Seed Vault (Norway). The radius of the circles is proportional to the number of samples deposited (Credit: PLOS ONE — Global Ex-Situ Crop Diversity Conservation and the Svalbard Global Seed Vault: Assessing the Current Status — Ola T. Westengen et al. 2013)
Figure 6: This is the filed where we have conducted one of the experiments to screen for plant with traits while also carrying the virtual ML/AI screening. The plants screened for heat traits lower their surface temperature.

OperAI develops Math and AI-based solutions to speed up and streamline operational processes at the edges of the cloud.

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