Microsoft announced the release of FarmVibes.AI, a collection of different open-source AI models that work towards supporting farm operators in optimizing their tasks and efficiency.
FarmVibes.AI is one of the numerous technologies created by Microsoft as part of an initiative named Project FarmVibes. This initiative aims to incorporate software and connected devices such as sensors to achieve higher farming efficiency. All the innovations released as a part of this initiative will be open-source.
“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”, said Ranveer Chandra, Microsoft’s managing director of research for industry.
FarmVibes.Connect includes a set of different software and hardware tools designed to deliver Internet connectivity to agricultural regions. Furthermore, this initiative leverages various unused elements of the radio spectrum to create wireless connectivity. FarmVibes.Edge, on the other hand, focuses on simplifying crop and farm data uploading to the cloud for analysis.
Four AI algorithms
FarmVibes.AI comprises four artificial algorithms which are designed to help farmers optimize day-to-day work by collecting data on crops.
The first algorithm, Async Fusion, is responsible for combining information collected from sensors at a farm, drones, and satellite imageries. It aims to deliver an optimal map for carrying out farming errands.
The second algorithm, SpaceEye, streamlines satellite data assessment and processing methods implemented in the farming maps. In the case there isn’t any satellite image, the AI algorithm substitutes it with satellite-based radar measurements and instruments.
The third algorithm, DeepMC, is great for estimating wind speeds, temperatures and rainfall through internet-connected devices. It identifies the optimal time for performing different agricultural tasks in certain weather conditions.
The fourth algorithm included in FarmVibes.AI helps farmers in all matters related to sustainability and diverse agricultural practices affecting carbon amounts in their soil. Not only this, the algorithm helps identify different methods of enhancing crop yields.
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