P3 Program Introducing New Technology, Teamwork to the Classroom
Plant Sensors Bringing Class Concepts to Life
Since its start in 2015, Iowa State University’s Predictive Plant Phenomics (P3) graduate program has focused on changing the narrative surrounding plant biology to increase crop productivity and meet industry demands for food and fuel. This semester, the program is kicking off a new initiative to bring what has been illustrated through textbooks and lectures to life, giving students the ability to track real-time data with the help of sensors designed to predict plant growth and productivity.
The main focus of the P3 program’s introductory course, “Foundations of Predictive Plant Phenomics,” has always been about the basics: teaching students how to look after corn and soybean plants — often for the first time — and how to conduct simple plant phenotyping, the process of analyzing physical and biochemical genetic traits. But thanks to the introduction of new sensors to the lab portion of the course, this semester, students in the class have the chance to dig in and design their own computational experiments to collect and analyze data, from soil moisture to atmospheric pressure.
“Biology has historically been thought of as too sloppy to use models and mathematic equations to predict,” said professor of genetics, development, and cell biology Carolyn Lawrence-Dill, who teaches the introductory class. “But computational resources and sensors have gotten to the point that we can measure at such high volume and accuracy that we can predict how plants will grow. Students learn by doing, and this project allows them to try things for themselves instead of just learning how these tools work in theory.”
The idea to expand the scope of the class beyond basic, manual phenotyping and into an interactive model that brings technology and collaboration into the lab environment was sparked earlier this year. Lawrence-Dill was inspired by Josh Peschel, assistant professor of agricultural and biosystems engineering, who keeps a suitcase full of sensors to demonstrate the equipment to the campus community. Based on Peschel’s suitcase, she assembled 10 sensor kits of her own, dubbed “Plant Cy-nce Toy Boxes,” each containing a credit card-sized Raspberry Pi computer, two Arduino Uno processers, a mix of plant sensors, including speech recognizers and motion sensors, and other equipment designed to help students get hands-on experience with the foundational elements of predictive plant phenomics.
As part of the newly revamped four credit course, once a week Lawrence-Dill’s students visit the Roy J. Carver Co-Lab greenhouse, where they work in groups to tinker with their toy boxes and perform collaborative research on a phenotypic area of their choice.
The P3 program is open to graduate students studying agronomy plant breeding, bioinformatics and computational biology, electrical and computer engineering, genetics and genomics, mechanical engineering, and plant biology. For many of the students who pursue the specialization, the Foundations of Predicative Plant Phenomics class, which is also open to students outside of the program’s accepted majors, is their first exposure to working with plants or learning how to code computers — or both. By design, the course requires students to work collaboratively and rely on each other’s specialties to tackle coding, data collection, and data analysis.
“I could sit in a lecture hall and have someone tell me how to hook up the sensors, how to code, but being able to tinker on your own really helps you learn,” said Ashlyn Rairdin, a first-year graduate student in plant biology and the P3 specialization. “It’s one of my favorite classes because of how much we get to collaborate and connect with each other.”
In the new initiative’s inaugural year, some students have opted to use the sensor kits to study carbon dioxide emissions by placing sensors in soil and under a soda bottle to capture and measure the gases being released. Others are using a machine learning algorithm to determine leaf angles on corn plants. One group is even attempting to build a robot that will, with the help of a LEGO brick body structure, be able to roam around the greenhouse on its own, capturing pictures of the plants and automatically scanning and reading sensors to track growth data.
“The entire concept of the class is tons of fun,” said Henri Chung, a first-year graduate student studying bioinformatics and computational biology with the P3 specialization. “The hands-on application helps with what I consider to be the most important part of graduate classes: how to apply classwork to your research. This course requires you to use computer science, engineering, and plant science in the same application; it’s one of the most multifaceted intro courses you could have.”
As a way to expand the reach of the course, starting next spring, Plant Cy-nce Toy Boxes will be available for checkout at no cost, allowing students to use the sensors for research in Lawrence-Dill’s class and beyond.