"One of the most consequential is weather, which can damage crop yields and livelihoods. A delayed monsoon, for example, can force a rice farmer in South Asia to replant or switch crops altogether, losing both time and income. Access to reliable, timely weather forecasts can help farmers prepare for the weeks ahead, find the best time to plant or determine how much fertilizer will be needed, resulting in better crop yields and lower costs."
"By using artificial intelligence, these models can deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models. This makes it possible for national meteorological agencies in developing countries to provide farmers with the timely, localized information about changing rainfall patterns that the farmers need. The challenge is getting this technology where it's needed. Why AI forecasting matters now The physics-based weather prediction models used by major meteorological centers around the world are powerful but costly."
Weather is a major and growing risk for farmers, damaging yields and livelihoods and forcing costly replants or crop switches when rainfall patterns change. Reliable, timely forecasts allow farmers to choose planting times, adjust fertilizer use, and reduce losses. Conventional physics-based forecasting models are accurate but require expensive computing infrastructure and have been optimized mainly for temperate, high-income regions. Since 2022, deep learning models have shown the ability to produce accurate short-term, localized forecasts at far lower computational cost. Lower-cost AI models can enable national meteorological agencies in developing countries to reach farmers with actionable forecasts, but deployment and access remain challenges.
Read at Fast Company
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