Reducing Water Waste through Data-Driven Irrigation Practices

Technological advances, artificial intelligence innovations, and widespread computing have all combined to necessitate a new generation of knowledge workers where data becomes a ubiquitous part of decision making (Sutton, 2006). Teaching today's students through the application of this "new" knowledge to long-established fields represents a key opportunity for innovation, improvement, and progress (Vasisht, et al., 2017). The authors present here their experience, and the accompanying materials developed, while working with high school students in a setting that intentionally combined "new" knowledge--derived from high-tech sensors and aerial imagery captured by a drone--with the longstanding practices and traditions of irrigation in agriculture. In this lesson, they teamed up with a local teacher and several students attending a rural high school to develop materials related to aerial imagery collected from an agricultural drone and an associated design challenge aimed at reducing water waste in irrigation practices. To introduce students to data and help them understand the imagery provided by drones in agricultural applications, the authors introduced them to the sensors involved, the image acquisition process, the data processing performed, and visualization techniques prior to them flying the drone. Although, like many of the existing drone software packages, the software utilized by the students in this scenario (FieldAgent) performed many data-processing and visualization steps automatically, they felt it was important to help students understand both the technology involved and the imaging pipeline prior to interpreting the data and utilizing their findings to inform their design decision making while attempting to devise solutions to reduce water waste in irrigation. They believe this emphasis on both data acquisition and application is important for influencing the students' designing and introduction to data-driven decision making.