FARMINGTON, Wash. – The gently rolling hills here in eastern Washington have long grown rich harvests of wheat, barley and lentils.
Fifth-generation farmer Andrew Nelson is adding a new bumper crop to that bounty: Data.
He gathers it from sensors in the soil, drones in the sky and satellites in space. They feed Nelson information about his farm at distinct points, every day, all year long — temperature variations, soil moisture and nutrient levels, plant health and more.
Nelson in turn feeds that data into Project FarmVibes, a new suite of farm-focused technologies from Microsoft Research. Starting today, Microsoft will open source these tools so researchers and data scientists — and the rare farmer like Nelson, who is also a software engineer — can build upon them to turn agricultural data into action that can help boost yields and cut costs.
The first open-source release is FarmVibes.AI. It is a sample set of algorithms aimed at inspiring the research and data science community to advance data-driven agriculture. Nelson is using this AI-powered toolkit to help guide decisions at every phase of farming, from before seeds go into the ground until well after harvest.
FarmVibes.AI algorithms, which run on Microsoft Azure, predict the ideal amounts of fertilizer and herbicide Nelson should use and where to apply them; forecast temperatures and wind speeds across his fields, informing when and where he plants and sprays; determine the ideal depth to plant seeds based on soil moisture; and tell him how different crops and practices can keep carbon sequestered in his soil.
Andrew Nelson studies a FarmVibes.AI image identifying grass weeds in one of his fields. It was created from multispectral drone imagery and will inform Nelson’s treatment decisions later this fall. (Photo: Dan DeLong for Microsoft)
“Project FarmVibes is allowing us to build the farm of the future,” said Nelson, who has partnered with Microsoft Research to turn his 7,500 acres into a proving ground for Project FarmVibes. “We’re showcasing the impact technology and AI can have in agriculture. For me, Project FarmVibes is saving a lot in time, it’s saving a lot in costs and it’s helping us control any issues we have on the farm.”
The new tools sprouted from Microsoft’s work with large customers like Land O’ Lakes and Bayer to integrate and analyze data. Project FarmVibes reflects more recent research in precision and sustainable agriculture.
By open sourcing its latest research tools, Microsoft wants to spread them far beyond Washington to help tackle the world’s urgent food problem, said Ranveer Chandra, managing director of Research for Industry.
By 2050, we’ll need to roughly double global food production to feed the planet, Chandra said. But as climate change accelerates, water levels drop and arable lands vanish, doing that sustainably will be a huge challenge.
“We believe one of the most promising approaches to address this problem is data-driven agriculture,” he said.
At Microsoft, we are working to empower growers with data and AI to augment their knowledge about farming and help them grow nutritious food in a sustainable way.
Research bears fruit
Until recently, Nelson’s farm was like many others around the world. He had internet in his home, but the Wi-Fi signal ended outside his door. His 7,500 acres were a dead zone.
Now he’s using a Project FarmVibes solution, called FarmVibes.Connect, which will eventually be open sourced by Microsoft to bring connectivity to remote and rural places. It delivers broadband access via TV white spaces, the unused spectrum that flickers as “snow” between channels. Today, Nelson has a solar-powered TV white spaces antenna that acts like a Wi-Fi router, but one that can cover most of his farm.
That connectivity has allowed him to glean insights from the FarmVibes.AI suite. Now available in GitHub, FarmVibes.AI includes:
Async Fusion, which combines drone and satellite imagery with data from ground-based sensors to offer insights. For example, Nelson uses Async Fusion to create nutrient heat maps from multispectral drone imagery and data from soil sensors. These maps are used to vary the rate at which he plants seeds and applies fertilizer, which can increase yield and prevent overfertilization. Async Fusion can also create soil moisture maps from sensor data across Nelson’s farm. These maps tell Nelson how deep to plant his seeds, and in what order he should plant his fields. As a bonus, they can help prevent tractors and sprayers from getting stuck in muck.
SpaceEye, which uses AI to remove clouds from satellite imagery. This helps Nelson fill in the gaps for areas he hasn’t scouted with a drone. He can then feed these images into AI models that can identify weeds, helping him create maps to deliver herbicide only to areas that need them. And even when he does spray, these maps let him vary the rate of application, delivering more volume to densely weedy patches and a lighter load elsewhere.
DeepMC, which uses sensor data and weather station forecasts to predict temperatures and wind speeds for his farm’s microclimate. In Nelson’s area, the local weather forecast predicts what conditions will be like 10 meters off the ground. “Well, I don’t care what it is 10 meters off the ground,” he said. “I care about what it is where my crops are.” Earlier this spring, Nelson was preparing to spray his wheat fields. He checked forecasts for the right weather window; the plants would be harmed by the herbicide if he sprayed in freezing temps. The local forecast looked promising, but DeepMC predicted a freeze. He held off spraying – and woke up to frost.
A “what if” analytics tool that estimates how various farming practices would affect the amount of carbon sequestered in the soil. Today, Nelson uses these “what if” scenarios to improve the health of his soil and boost yield. But he plans to use them to enter carbon markets, which pay farmers for practices that keep carbon dioxide locked up in soil rather than entering the atmosphere.
