CRSS 4280/6280
Programming Precision Ag.
(UGA Spring 2025)

Hi there!

This is the welcome page for the 2025 CRSS 4280/6280 Programming Precision Ag taught by Dr. Leo Bastos.

Below you will find:

Prep material before first lab
Schedule with links for slides, code, and recordings
Course syllabus

Important links

Prep material before first lab

To be ready for next class (Intro to R), please follow the link below and complete all tasks:

Lab 01 prep

If you have questions or issues, email me before class.

Schedule

Chalkboard

date	time	topic
Tue, Jan 07	2:20 to 4:00	Welcome and Intro
Thu, Jan 09	2:20 to 4:20	R & RStudio intro Rstudio Projects Rmarkdown Assignment #1 - data viz
Tue, Jan 14	2:20 to 4:00	Data wrangling Assignment #2 - data wrangling
Thu, Jan 16	2:20 to 4:20	Coordinate reference system Geospatial data types CRS and EPSG
Tue, Jan 21	2:20 to 4:00	USDA NASS data, tables and plots pt 1
Thu, Jan 23	2:20 to 4:20	USDA NASS tables and plots
Tue, Jan 28	2:20 to 4:00	quarto reports USDA spatial data USDA NASS mini-project
Thu, Jan 30	2:20 to 4:20	Digitizing field boundary Assignment #3 - yield editor paper
Tue, Feb 04	2:20 to 4:00	Open source soils data Open source elevation Assigment #4 - Open data
Thu, Feb 06	2:20 to 4:20	Yield monitor wrangling
Tue, Feb 11	2:20 to 4:00	Yield editor Assignment #5 - yield editor 19 and 20
Thu, Feb 13	2:20 to 4:20	Yield interpolation Assignment #6 - yield interpolation 19 and 20
Tue, Feb 18	2:20 to 4:00	Yield spatiotemporal stability pt 1 Assignment #7 - yield stability paper
Thu, Feb 20	2:20 to 4:20	Terrain
Tue, Feb 25	2:20 to 4:00	Soil ECa pt 1
Thu, Feb 27	2:20 to 4:20	Soil ECa pt 2
Tue, Mar 04	2:20 to 4:00	No class - Spring Break
Thu, Mar 06	2:20 to 4:20	No class - Spring Break
Tue, Mar 11	2:20 to 4:00
Thu, Mar 13	2:20 to 4:20	Spatiotemporal yield validation
Tue, Mar 18	2:20 to 4:00	Creating management zones
Thu, Mar 20	2:20 to 4:20	Validating management zones Creating zone-based VRx pt 1 Assignment #8
Tue, Mar 25	2:20 to 4:00	Creating zone-based VRx pt 2 Final project
Thu, Mar 27	2:20 to 4:20	Imagery-based VR Nitrogen pt 1
Tue, Apr 01	2:20 to 4:00	Imagery-based VR Nitrogen pt 2
Thu, Apr 03	2:20 to 4:20	Satellite imagery - Resolutions - Open-data satellites - Satellite APIs
Tue, Apr 08	2:20 to 4:00	PlanetScope Imagery processing pt 1
Thu, Apr 10	2:20 to 4:20	PlanetScope Imagery processing pt 2
Tue, Apr 15	2:20 to 4:00	PlanetScope Imagery processing pt 3
Thu, Apr 17	2:20 to 4:20	PlanetScope Imagery stability analysis
Tue, Apr 22	2:20 to 4:00	PlanetScope Imagery stability analysis pt 2
Thu, Apr 24	2:20 to 4:20	Profitability analysis
NA, NA	NA	No class - NA

Course Syllabus

Course information

General information

CRSS 4280/6280 - Programming Precision Agriculture
Spring Semester 2025
3 credit hours

Meeting times and locations

Lectures: Tuesdays at 02:20-04:00 pm
Labs: Thursdays at 02:20-04:20 pm
Location:
- Athens campus: in person at 1203 Miller Plant Sciences
- Tifton campus: in person at 601 NESPAL South OR remote
- Griffin campus: in person at 107 SLC OR remote

Prerequisites

CRSS 3030 – Principles of Precision Agriculture

Co-requisites

CRSS 4050 / 6050 – Improving Nutrient and Energy Efficiency with Geographic Information Systems or equivalent GIS course.

Instructors information

General information

Dr. Leonardo M. Bastos, Assistant Professor
Crop & Soil Sciences Dept.
4101 Miller Plant Sciences Building, Athens Campus
University of Georgia
Email: lmbastos@uga.edu
URL: leombastos.github.io/bastoslab/

Nushrat Niva, PhD student
Department of Crop & Soil Sciences
Miller Plant Sciences Building, Athens Campus
University of Georgia
Email: Nushrat.Niva@uga.edu

Office hours

The TA office hours are Monday and Thursday from 9 to 11 am, upon appointment. Please email Niva if you need help to get a time scheduled within the windows above.

Course description and details

Description

The course will emphasize sampling plan, obtaining and processing, and using various agricultural geospatial data layers for decision making related to precision agriculture. Data types used in the course include publicly available like weather, soils, and satellite remote sensing, and private data like yield monitor, Veris-derived soil electrical conductivity and elevation, and drone remote sensing. Data processing steps include cleaning, removal of outliers, normalization, interpolation, and drone imagery processing. Analytical techniques include clustering for creation of management zones, zone-based and imagery-based variable rate prescription, profitability maps, and use of satellite remote sensing for crop scouting. All steps will be conducted utilizing the R statistical language to create well documented and reproducible precision agriculture analytical workflows. No previous knowledge in R is required.

Course learning outcomes

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

Intro to R
Using R as a geographic information system (GIS)
Accessing publicly available geospatial data through R
1. Crop statistics from USDA NASS
2. Soils data from SSURGO
3. Weather and satellite remote sensing
4. State and county boundaries
Digitizing field boundaries
Creating grids for soil sampling
Geostatistics for point-data interpolation Inverse distance weighting
Yield monitor data processing
1. Understand the sources of error
2. Cleaning and removal of erroneous data points
3. Spatial and temporal yield variability analysis
Terrain data processing
1. Elevation
2. Aspect
3. Slope
Management zones
1. Combining yield and soils data
2. Creating management zones with cluster analysis
3. Validating management zones
Variable rate prescription
1. Zone-based variable rate prescription
2. Imagery-based variable rate prescription
Profitability maps
Satellite remote sensing for crop scouting
Course final project

The topical outline is a general plan for the course; deviations announced to the class by the instructor may be necessary.

Course materials

Textbook

A textbook is not required. Reading materials will be supplied by instructor and will include benchmark research articles, manuals, and other materials.

Technology and software requirements

Students will need to have access to:

A computer (to install software, code along with instructors)
A second screen (main screen to code along, second screen to watch class if not in person)

If a student does not have access to these resources (personal laptop/desktop and a second screen), please let instructors know to ensure proper accommodations can be made.

Course website

Important links related to this course:

Assessment and Grading

Grading categories

The grade you receive in this course will be determined from your performance on a mini-project, one mid-term exam, periodic quizzes, homework assignments and lab reports, a final project, and class participation. These factors will be weighted as follows:

Activity	Grade
Mini-project: USDA NASS data	10%
Mid-term exam	10%
Homework assignments	35%
In-class quizzes	15%
Final project: from raw data to zone-based variable rate	20%
Class participation	10%

Written assignment quality

Up to thirty percent of the grade on written assignments (mini-project, homework, final project) will be based on quality of communication.

Spelling, grammar, punctuation, and clarity of writing are evidence of written communication quality.

Class participation

Active class participation is important for you to achieve the learning goals of the class. To receive maximum credit for class participation you must

attend every class period (lecture or laboratory)
arrive on time and remain for the entire class period
you are actively engaged and attentive throughout the class period
participate in the class discussion and ask and answer questions

Grading scale

Final grades will be assigned as follows:

Letter	Grade
A	93 and above
A-	90-92
B+	87-89
B	83-86
B-	80-82
C+	77-79
C	73-76
C-	70-72
D+	67-69
D	63-66
D-	60-62
F	59 and below

Extra credit opportunities

Extra credit opportunities may be made available during projects, homework assignments, and exams, at the discretion of the instructor.

Course statements and policies

Academic honesty

UGA Student Honor Code: “I will be academically honest in all of my academic work and will not tolerate academic dishonesty of others.” A Culture of Honesty, the University’s policy and procedures for handling cases of suspected dishonesty, can be found at www.uga.edu/ovpi.

For this course, all lab reports, projects, and other assignments can be discussed with your classmates but any work you turn in must be your own.

Students can work together through coding exercises, but direct copying and pasting from a colleague will be considered plagiarism.

If using code from an online source, it is ok to copy and paste IF proper credit is given (e.g., showing the website source from where the code was obtained).

Unless explicitly stated, artificial intelligence-based technologies, such as ChatGPT, must not be used to generate responses for student assignments.

Attendance policy

Students are expected to attend every class period.

Students on the Athens campus must attend class in-person. If a special circumstance arise (illness, travel, etc.), student absence or remote attendance must be informed to instructors prior to that class period.

Students on the Tifton and Griffin campuses may attend class in-person on their campuses or remote using the zoom link information. Student absence must be informed to instructors prior to that class period.

Per Board of Regents policy, I reserve the right to drop students from the class roll who miss more than 5 class periods unexcused. Such students will be given a WF grade.

Disclaimer

The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.

Make-up procedures

There will be no make-ups for missed quizzes. Any missed quiz will be recorded as a zero
Exams can be made up only with a note from a doctor or if you can document extenuating circumstances. Any unexcused missed exam will be recorded as a zero
Homework assignments will be accepted up to one week beyond the due date. The penalty for submitting a late assignment is one letter grade
Homework assignments may be submitted late without penalty in case of illness, extenuating circumstances, or if prior arrangements are made with the instructors. All late assignments are due within a week of the original due date or within a week of when a student returns from an illness

Mental Health and Wellness Resources

If you or someone you know needs assistance, you are encouraged to contact Student Care and Outreach in the Division of Student Affairs at 706-542-7774 or visit https://sco.uga.edu/. They will help you navigate any difficult circumstances you may be facing by connecting you with the appropriate resources or services.
UGA has several resources for a student seeking mental health services (https://www.uhs.uga.edu/bewelluga/bewelluga) or crisis support (https://www.uhs.uga.edu/info/emergencies).
If you need help managing stress anxiety, relationships, etc., please visit BeWellUGA (https://www.uhs.uga.edu/bewelluga/bewelluga) for a list of FREE workshops, classes, mentoring, and health coaching led by licensed clinicians and health educators in the University Health Center.
Additional resources can be accessed through the UGA App.

Disability statement

If you plan to request accommodations for a disability, please register with the Disability Resource Center. They can be reached by visiting Clark Howell Hall, calling 706-542-8719 (voice) or 706-542-8778 (TTY), or by visiting https://sitedrc.uga.edu

Resources

Below there are some resources for students to further your knowledge in topics ranging from using quarto files, vector and raster manipulation in R, data visualization, and geostatistics.