According to McKinsey, the agricultural sector is the least digitized industry in the U.S. Yet, agriculture already produces large volumes of data — especially from IoT. There’s hardly a lack of scenarios for digitizing farming, with the common thread for all the use cases is that they each involve Big Data.
Among the obvious use cases, precision farming uses sensory data to tell farmers exactly where to plant and how much to water and how to fertilize. With a baseline of rules, this provides an excellent case for using machine learning that can subsequently adapt those rules to the specifics of the actual field and correlation with yield data. Food safety and spoilage prevention can be enhanced through use of smart devices that detect ambient humidity, temperature, chemical contamination, and the presence of gasses signaling the presence of harmful microbes.
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