According to McKinsey, the agricultural sector is the least digitized industry in the U.S., writes Tony Baer on ZDNet.com. 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.
There are other Big Data use cases not necessarily specific to agriculture with the potential for eliminating cost and waste such as asset management and predictive/prescriptive maintenance of farm equipment, and supply chain optimization for getting crops efficiently to market.
But Big Data on the farm is a chicken-or-egg scenario. The use cases are there. But then there’s the matter of infrastructure, data ownership, and economics. Which one has to come first?