Micro-climate predictions: Enabling hyper-local decisions for agriculture and renewables

It is springtime in Eastern Washington, USA, and the temperature is slightly above freezing. A farmer is preparing to fertilize his fields of wheat and lentils as winter runoff and frost are nearly finished. The plants are susceptible to fertilizer at freezing temperatures, so the farmer checks forecasts from the local weather station, which is about 50 miles away. The three-day outlook shows temperatures above freezing. The farmer rents equipment and starts fertilizing the farm. But at night, the temperature in parts of the fields drops below freezing and kills around 20% of the crops. This is unfortunately a common situation, since climatic parameters can vary over short distances and even between sections of the farm.

To address this problem and others, we developed DeepMC, a framework for predicting micro-climates, or the accumulation of climatic parameters formed around a relatively small, homogeneous region. Micro-climate predictions are beneficial in agriculture, forestry, architecture, urban design, ecology conservation, maritime and other domains. DeepMC predicts various micro-climate parameters with over 90% accuracy at IoT sensor locations deployed around the world.

This work is a part of a Microsoft Research initiative, Research for Industry, which aims to address challenges including climate change, pandemics, and food security through technological breakthroughs. To learn more about the work Microsoft is doing to enable data-driving farming, check out the FarmBeats: AI, Edge, and IoT agriculture project page.

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