0 or 3 credits
Fall 2025 Lecture Laboratory Upper Division
Introducing concepts, models, algorithms, and tools in plant phenotyping development and application projects. Class topics include high-throughput phenotyping in greenhouse, field phenotyping platforms, Ag remote sensing, plant sensors (hyperspectral, 3D thermal, florescent, X-ray, etc.), plant image processing technologies, statistical modeling, big data, database requirement, artificial intelligence algorithms, and hybridizations of the above techniques applied in plant phenotyping. Permission of instructor required.
Learning Outcomes1Know the concept of digital agriculture and plant sensors' applications.
2Understand the plant phenotyping technologies, in both software and hardware.
3Learn the skills of designing a professional phenotyping imaging system and collecting data.
4Understand the current phenotyping activities in academia and industry.
5Know the major plant sensors and their applications.
6Have the capability of choosing or designing the most feasible sensor system for specific phenotyping projects.
7Understand the role of the plant sensors in a precision agriculture system.
8Learn basic image processing skills.
9Have the capability of applying computational intelligence and machine learning techniques to classification, prediction, pattern recognition, and optimization problems.
Restrictions
made by Purdue students.