Big Data and the Internet of Things Drive Precision Agriculture
By 2050, the world will be populated with an estimated 9 million people, and in order to feed all of them, agricultural output will need to double. Information technology, in the form of the Internet of Things (IoT), wireless and mobile technologies, and automated data collection and analysis is likely to provide part of the solution to this problem.
Purdue University’s College of Agriculture is one of the organizations leading the way toward more data-driven farming. The College has developed an agriculture-oriented network with advanced IoT sensors and devices that will allow researchers to study and improve plant growth and food production processes. According to Pat Smoker, director of Purdue Agriculture IT, in West Lafayette, Indiana, every process from farm to table has potential for improvement through better use of information technology.
Purdue College of Agriculture partnered with Hewlett Packard Enterprise (HPE) on a digital agriculture initiative. In fall 2016, the university began installing an Internet of Things (IoT) network on its 1,408-acre research farm, the Agronomy Center for Research and Education (ACRE). The system captures terabytes of data daily from sensors, cameras, and human inputs. To collect, aggregate, process, and transmit such large volumes of data back to Purdue’s HPE supercomputer, the university is deploying a combination of wireless and edge computing technologies (see Chapters 5 and 7). They include solar-powered mobile Wi-Fi hotspots, an adaptive weather tower providing high-speed connectivity across the entire ACRE facility, and the PhenoRover, a semi-automated mobile vehicle that roams throughout ACRE research plots capturing real-time data from plant-based sensors. Purdue is also experimenting with drones for plant-growth data collection. ACRE researchers can enter data into a mobile device on-site and transmit them via the wireless network to an HPE data center for analysis.
Previously, Purdue’s faculty had to figure out how to transmit data from the sensors back to the lab, and assign someone to write the software for analyzing the data. The new system is faster and responsive. For example, researchers using mobile devices in the field can transmit data about seed growth back to ACRE labs to analyze the impact of water levels, fertilizer quantities, and soil types. The labs can then communicate the results of their analysis back to the field to allow quick adjustments. Computerized instructions control how planting and spraying machines apply seed and nutrients to a field.
The Purdue project is an example of “precision agriculture,” in which data collected and analyzed with digital tools drive decisions about fertilizer levels, planting depth, and irrigation requirements for small sections of fields or individual plants, and automated equipment can apply the ideal treatment for specific weeds.
Large agricultural companies like Monsanto and DuPont are big precision agriculture players, providing computerized data analysis and planting recommendations to farmers who use their seeds, fertilizers, and herbicides. The farmer provides data on his or her farm’s field boundaries, historic crop yields, and soil conditions to these companies or another agricultural data analysis company, which analyzes the data along with other data it has collected about seed performance weather conditions, and soil types in different areas. The company doing the data analysis then sends a computer file with recommendations back to the farmer, who uploads the data into computerized planting equipment and follows the recommendations as it plants fields. For example, the recommendations might tell an Iowa corn farmer to lower the number of seeds planted per acre or to plant more seeds per acre in specified portions of the field capable of growing more corn. The farmer might also receive advice on the exact type of seed to plant in different areas and how much fertilizer to apply. In addition to producing higher crop yields, farmers using fertilizer, water, and energy to run equipment more precisely are less wasteful, and this also promotes the health of the planet.
Sources: “Envision: The Big Idea,” https://ag.purdue.edu, accessed April 26, 2018; “Precision Agriculture,” www.farms.com, accessed April 26, 2018; www.monsanto.com, accessed May 1, 2018; and Eileen McCooey, “Purdue Uses IoT to Reinvent Farming, Boost Output,” Baseline, December 6, 2017.
Precision agriculture is a powerful illustration of how information systems can dramatically improve decision making. In the past, deciding what to plant, how, where, and when was based on farmers’ historical experience with their land and best guesses. Wireless networks, myriad sensors in the field, mobile devices, powerful computers, and big data analytics tools have created systems that can make many of these decisions much more rapidly and accurately.
The chapter-opening diagram calls attention to important points raised by this case and this chapter. There is a worldwide need to increase food production, both to feed a rapidly growing global population and to make farms more profitable. Wireless technology and big data analytics create new opportunities for managing crops almost on a plant-by-plant basis. Managing fields with this level of computerized precision means farmers need to use less fertilizer and less seed per unit of land, potentially saving an individual farmer tens of thousands of dollars while increasing crop yields. Precision agriculture may also help solve the world food crisis.
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Here are some questions to think about: How is information technology changing the way farmers run their business? How is precision agriculture changing decision making? Give examples of two decisions that can be improved by using precision agriculture.
