Course Description
Covers the concepts and practical skills for plant phenotyping, from traditional methods to modern advancements. Includes imaging systems, data management, Python programming for phenotypic analysis, and lab activities focused on research experiments, imaging, data analysis, presentations, and scientific writing. Suitable for beginners and those with programming experience.
Additional Requirements for Graduate Students:
There will be a final project starting in the middle of the semester. Undergraduate students are encouraged to form a team with 2-3 people per team and pick the project from the list designed for undergraduate students. The project report and final presentation will be conducted in the unit of a team. Graduate students are required to pick the project from a list designed for the graduate level, and must complete the project individually, including the final report and presentation.
Athena Title
Plant Phenotyping Tech
Prerequisite
(STAT 2000 or STAT 2000E) or (PHYS 1112-1112L) and (HORT 2000 or HORT 2000E) or (BIOL 1107 or BIOL 2107H or PBIO 1210) or permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
- By the end of this course, students should be able to introduce and explain the principles of plant phenotyping technologies, including the ability to identify the advantages and limitations of each type of system.
- By the end of this course, students should be able to list, explain, and quantify multiple critical plant phenotypes and analyze the phenotypic traits in digital images by developing a Python program.
- By the end of this course, students should be able to design and implement a plant imaging system for a specific real-world application and have the ability to diagnose its limitations and interpret the data.
- By the end of this course, students should be able to work on a research project in a team with independent responsibility and perform self-reflection.
- By the end of this course, students should be able to present and document the project results in professional oral presentations and scientific reports.
Topical Outline
- 1. Introduction of plant phenotyping
• Introduction to phenotyping, including its importance and applications in agriculture
o Shoot, root, fruit, and seed phenotypes
o Importance in breeding, crop protection, product quality control, etc.
• Overview of traditional and modern phenotyping methods
- 2. Essential knowledge basics about plant phenotyping equipment
• Basics in optical systems and electronic systems
o Components of an optical system, what is light, how photons are captured and transformed into information
• Basics in camera systems
o RGB camera, 3D camera, image distortion, color calibration
• Spectral imaging technology
o Multispectral, hyperspectral
• High-throughput plant phenotyping
o Introduction of automation systems, robotics, remote sensing
- 3. Process and interpretation of plant phenotyping data
• Basics in data management
o How data is stored, organized, corrected, and analyzed
• Basics in Python programming language. (Lectures and labs might be hosted in the computer lab if not all the students have a laptop)
o Python Hello World. Will be taught using Google Colab notebooks
• Basics in computer vision algorithms and spectral analysis
o The famous OpenCV library
• Experiment design and project preparation
o How to design your own plant phenotyping experiments based on what you need and what you have
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
