Principles of engineering, data analysis, and plant sciences and their interplay applied to predictive plant phenomics. Transport phenomena, sensor design, image analysis, graph models, network data analysis, fundamentals of genomics and phenomics. Multidisciplinary laboratory exercises.
Various resources are available. Use online dictionaries where jargon is prevalent.
Enable students to speak across engineering, data science, and plant science disciplines. Understand how biological concepts fit with computational representations of the data.
Student Learning Outcomes:
- Students will achieve fundamental understanding of how plant science, engineering, and data science can be used together for research.
- Students will be able to formulate a hypothesis and then design computational experiments that test that hypothesis.
- Students will be able to take information learned in one context and transfer/apply it to a different context.
- Students will be able to articulate ideas outside their own primary disciplines and find others to work with where their skills are insufficient to pursue meaningful research.
- Students will be able to graphically represent experimental data in a meaningful way.
Instructors can grade in any way they see fit. One possible mechanism for assessment that was a part of the Iowa State University course was to require the submission of a paragraph describing the utility of the day’s lecture to the student’s own research or research interests and one lecture was delivered by each student in the class rather than an outside expert. The students selected a topic in keeping with their existing knowledge and expertise.
Suggested Order of Lectures:
(link to all recordings: https://vimeo.com/showcase/8796225 )
|Lecture/Instructor||Topic (link to video)|
|Lecture 1; Carolyn Lawrence-Dill||How to present a lecture|
|Lecture 2; Carolyn Lawrence-Dill||How to visualize and present data|
|Lecture 3; Theodore Heindel||Balances|
|Lecture 4; Theodore Heindel||Fluid flow|
|Lecture 5; Theodore Heindel||Heat transfer - conduction|
|Lecture 6; Theodore Heindel||Heat transfer - convection|
|Lecture 7; Theodore Heindel||Heat transfer - radiation|
|Lecture 8; Theodore Heindel||Heat/mass transfer|
|Lecture 9; Ashlyn Rairdin (former student)||Central dogma|
|Lecture 10; Kaitlin Higgins (former student)||Genetics, cell cycle, and transmission|
|Lecture 11; Leila Fattel||Comparative genomics|
|Lecture 12; James McNellie (former student)||Genome-wide association studies - GWAS|
|Lecture 13; Samantha Snodgrass (former student)||Plant anatomy|
|Lecture 14; Yanhai Yin||Plant development|
|Lecture 15; Oluwatoyosi Akintayo (former student)||Plant physiology|
|Lecture 16; Tyler Foster (former student)||Photosynthesis|
|Lecture 17; Gwyn Beattie||Microbiomes and metagenomes|
|Lecture 18; Keting Chen||Metabolomics - protecting plant surfaces|
|Lecture 19; Kevin Chiteri (former student)||GMOs|
|Lecture 20; Kendall Lamkey||Constraints to production|
|Lecture 21; Danny Singh||Cultivar development and the role of phenotyping|
|Lecture 22; Juan Panelo (former student)||Yield potential|
|Lecture 23; Fatemeh Amini||Improvement in plant breeding using optimization and machine learning|
|Lecture 24; Julie Dickerson||Network topology analysis|
|Lecture 25; Julie Dickerson||Network inference and module analysis|
|Lecture 26; Henri Chung (former student)||Random forest principles|
|Lecture 27; Soumik Sarkar||Introduction to machine learning|
|Lecture 28; Talukder Zaki Jubery||Image processing I|
|Lecture 29; Talukder Zaki Jubery||Image processing II|
|Lecture 30; Jodi Callwood (former student)||Image-based learning|
|Lecture 31; Katerina Holan (former student)||Image-based quantification of Puccinia sorghi pustules in maize|
|Lecture 32; Colleen Yanarella (former student)||Language-based phenotyping|
|Lecture 33; Iddo Friedberg||Crowdsourcing and competitions|
|Lecture 34; Priyanka Jayashankar||Farmers' perceived barriers and benefits about adopting technology|
This material is based upon work supported by the National Science Foundation under DGE#1545463 and the United States Department of Agriculture-National Institute of Food and Agriculture Grant number 2020-68013-30934.