Course Description
Students will be exposed to planning, collecting, processing, and analyzing agricultural geospatial data used in precision agriculture. Data types include crop, soil, elevation, remote sensing, and yield monitoring. All steps will be conducted utilizing the R statistical language to create well-documented and reproducible precision agriculture analytical workflows.
Additional Requirements for Graduate Students:
Graduate students will be required to develop further both the mini-project and final project with extra activities related to data analysis and report delivery.
Athena Title
Prog Precision Ag
Prerequisite
CRSS 3030
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
The course objectives are to provide students with a good understanding of the emerging areas in precision agriculture, the ability to find, analyze and evaluate spatially distributed data sets, and the experience to use key precision agriculture tools and technologies. Additional goals are to: • Extend critical thinking and problem-solving abilities • Improve written and oral communication skills • Learn how to create, present, and interpret maps • Create reproducible analytical workflows using R
Topical Outline
1. Intro to R 2. Using R as a geographic information system (GIS) 3. Accessing publicly available geospatial data through R a. Crop statistics from USDA NASS b. Soils data from SSURGO c. Weather and satellite remote sensing from Google Earth Engine d. State and county boundaries 4. Creating grids for soil sampling 5. Geostatistics for point-data interpolation with a. Inverse distance weighting b. Kriging 6. Yield monitor data processing a. Understand the sources of error b. Cleaning and removal of erroneous data points c. Spatial and temporal yield variability analysis 7. Terrain data processing a. Elevation b. Aspect c. Slope 8. Management zones a. Combining yield and soils data b. Creating management zones with cluster analysis c. Validating management zones 9. UAV remote sensing a. Data collection planning b. Imagery processing 10. Variable rate prescription a. Zone-based variable rate prescription b. Imagery-based variable rate prescription 11. Profitability maps 12. Satellite remote sensing for crop scouting 13. Course project: from raw data to management zone prescription
Syllabus
Public CV