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Big Data + Precision Agriculture: Advancing the Science of Farming
Precision agriculture is a technique for collecting and processing data in real time to help farmers make decisions about how best to plant, fertilize and harvest crops. The technology uses of sensors monitoring plants in the field and the use of field pictures from satellite and drone imagery. The data collected is fed into models and simulations to predict future farm conditions.
The concept of precision agriculture actually predates newer technologies like big data, machine learning/deep learning and IoT. Integration of these new technologies and recent advancement in the area of data analytics can only make the use of precision agriculture even more precise.
Prithviraj Lakkakula, a research assistant professor at North Dakota State University, wrote that “precision agriculture provides an input for big data analytics. We could consider precision agriculture and big data complementary to each other. Several applications of machine learning and big data in agriculture include information on particular crop/commodity seeds sold in a season, Google satellite imaging, pest and/or disease detection using satellite images, drone usage, predictions of commodity supply and demand, and water supplies for assessing drought or floods.
Andrew Brust, entrepreneur and CTO, commented on agriculture technology developments, saying that “Computer vision/imaging has serious applicability in this domain, as the capture of images combined with pattern recognition technology can help detect crop disease and, on an automated basis, dispatch personnel to address it. It can also help alert farmers to where they need to prune and harvest. So not only is the data collection made more economical, but the methodical analysis of the collected data, and the dispatch of responsive action, is made more feasible and economical as well.”
