Farm to every table via precision agriculture

Purdue College of Engineering
Purdue Engineering Review
4 min readNov 11, 2020
IoT4Ag researchers will work in three interconnected projects: sensing, communication/energy and response. Tiny, plantable sensors will need to send data to robots and other farm equipment, all of which will also need to be able to talk to the cloud. Finally, all of this data must be integrated with that from the wider internet, and fed back to farmers so they can make better decisions. (Illustration credit: Julie Colton)

It’s estimated that the global population will grow by about one-fourth, to nearly 10 billion people, by 2050, requiring a large increase in the food supply. We aim to meet that need through precision agriculture — the use of technology to measure and analyze data from the field in order to manage energy, water and agrochemical inputs, as well as to enhance crop yield and farm profitability.

Current methods for monitoring crop conditions are manual, labor-intensive and costly. While the concept of precision agriculture has been around for more than three decades, information technology now provides an opportunity to fulfill the urgent need for new physical and cyber-physical systems that can sense and respond to multiple variables in the field with higher resolution, increased specificity, greater speed, and enhanced autonomy.

The National Science Foundation is supporting this effort with its recently launched Engineering Research Center for the Internet of Things for Precision Agriculture (IoT4Ag). The mission is to create and translate to practice IoT technologies for precision agriculture as well as train and educate a diverse workforce that will address the societal grand challenge of food, energy, and water security for decades to come. That requires expertise in agronomy, agricultural engineering, economics, environmental science, and cyber-physical systems.

Technology innovations have permeated agriculture for a long time: think tractors replacing horses. Today’s tractors and other farm machinery use global navigation satellite system (GNSS)-enabled navigation, drone technology, and data analytics to improve productivity. With these technologies coupled with an IoT backbone of connected devices that sense and transmit data, estimates indicate water use can be reduced by 30 percent and energy costs can be cut by almost 50 percent.

The time is right to develop these next-generation technologies that deliver high-resolution, rapid and chemically-specific soil and plant data. This is vital to better manage crops, overcome the tens of billions of dollars in economic loss each year to pathogens, and realize more crop for every drop of water or Joule of energy.

Purdue faculty will play key roles across the IoT4Ag research thrust areas of Agricultural Sensor Systems, Communication and Energy Systems, and Agricultural Response Systems. We will be working in robotics to develop novel aerial and ground drones with advanced pattern recognition and image analysis algorithms for agricultural mapping and sensing. In addition, we’ll develop the decision architecture a distributed, energy-efficient machine learning pipeline for streaming sensor data. And we’ll create and deploy a suite of communications and energy technologies, tailored to a diverse set of sensors, platforms and environmental conditions.

The IoT4Ag Center features a large outreach effort in Workforce Development, Diversity & Inclusion, and Innovation. We will develop and deploy these programs in schools, after-school activities, camps, and libraries and museums. We’ll work with groups like Deaf Kids Code and Future Farmers of America, as well as partner with community colleges to help train students in IoT4Ag skills and transition them to bachelor’s degree programs.

At the university level, the center, headquartered at the University of Pennsylvania’s School of Engineering and Applied Science, will establish the Pathway to PhD Program (PPP) to increase the number of successful graduate school and graduate fellowship program applicants especially among women and underrepresented minorities. At the professional level, we will leverage agricultural extensions at Purdue, University of California Merced, and the University of Florida to increase precision agriculture technology competency and to support certification of the ag workforce.

We envision the farms of the future will use autonomous farm machinery to plant a network of sophisticated biodegradable IoT sensors in the ground. They will deploy aerial and/or ground drones for wireless interrogation of these sensors, pulling data from the field, relaying sensor data between machines, and storing and analyzing it in a central repository.

This data will be available throughout the growing season at very fine spatial and time scales to enable specific decisions and precise interventions at optimal times. This advance will spur dramatic increases in productivity and decreases in resources needed to field a healthy crop.

Our target 10-year outcome is to pair transformative science and engineering-based integrated systems for precision agriculture with a diverse, well-educated workforce to generate more crop for every drop of water or Joule of energy, assure sustainable agricultural processes, and realize a $47 billion annual increase in U.S. crop market value.

David J. Cappelleri, PhD

Purdue Site Director, IoT4Ag Center

Associate Professor, School of Mechanical Engineering

College of Engineering, Purdue University

Related Links

Purdue University to collaborate in NSF-funded Engineering Research Center to develop the Internet of Things for Precision Agriculture

Related news release on IoT4Ag

IoT4Ag website

Forbes: National Science Foundation invests $104 million to launch four new Engineering Research Centers

Inside Indiana Business: Purdue joins Ag IoT research collaboration

Purdue Engineering Podcast: Digital agriculture (including discussion of IoT4Ag)

Purdue Engineering Review: New farm field hands: autonomous robots

Multi-Scale Robotics and Automation Lab (MSRAL)

--

--