CPSC 499

Fall 2026 All Classes

All Classes

Credit: 1 TO 4 hours.

Advanced experimental course on a special topic in crop sciences.

1 to 4 undergraduate hours. 1 to 4 graduate hours. Approved for Letter and S/U grading. May be repeated if topics vary.

CPSC 499 class schedule data for fall 2026
Status CRN Type Section Time Day Location Instructor Section Details
3
10564
Independent Study
ARRANGED
n.a.
Location Pending
Availability:
Open (Restricted)
Part of Term:
1
Date Range:
08/24/26-12/09/26
Special Approval:
Instructor Approval Required
1
81704
Laboratory
Lecture-Discussion
AM
AM
2:00PM -5:00PM
11:00AM -11:50AM
R
TR
W13 Turner Hall
W223 Turner Hall
Preza Fontes, G
Preza Fontes, G
Availability:
Open
Part of Term:
1
Date Range:
08/24/26-12/09/26
Credit:
3 hours
Section Title:
Agronomic Management
Section Info:
This course explores the principles and practices of soil, water, and nutrient conservation in agricultural environments. Students will examine the physical, chemical, and biological processes that influence resource management and learn strategies to maintain productivity while promoting environmental stewardship. Emphasis is placed on identifying conservation needs, evaluating current research, and understanding the social and policy implications of resource management. Students will design integrated conservation plans that address real-world challenges in sustainable agriculture.
1
79918
Lecture-Discussion
AWM
1:00PM -2:20PM
WF
316S Mumford Hall
Peng, B
Availability:
Open
Part of Term:
1
Date Range:
08/24/26-12/09/26
Credit:
3 hours
Section Title:
Agricultural Hydrology
Section Info:
Junior/Senior-level undergraduate and entry-level graduate course focusing on improving the students’ systems-level understanding of water-agriculture nexus from field to watershed scales under climate change and land use intensification. We will focus on the process understanding of agricultural hydrology (e.g. soil hydrology, soil-plant-atmosphere continuum, watershed hydrology, reactive transport and water quality), practical applications (e.g. drainage management, irrigation management, and conservation management), societal impacts (e.g. food production, nutrient loss reduction, and environmental sustainability), and the start-of-the-art methodology to conduct research related to agricultural hydrology (e.g. field data collection, lab analysis, satellite remote sensing, and numerical modeling, environmental data science, and artificial intelligence).
1
81400
Laboratory-Discussion
JDJ
9:00AM -12:00PM
F
W9 Turner Hall
Jones, J
Availability:
Open
Part of Term:
1
Date Range:
08/24/26-12/09/26
Credit:
3 hours
Section Title:
Soil & Plant Nutrient Analysis
Section Info:
This undergraduate and graduate-level course provides hands-on training in the measurement, interpretation, and management of soil and plant nutrients for crop production systems. Students will develop a working understanding of nutrient bioavailability, soil and plant sampling techniques, and laboratory analytical methods used in modern soil fertility programs. Through weekly laboratory exercises, students will generate, analyze, and interpret soil and plant nutrient data and connect analytical results to agronomic decision-making. The course culminates in a site-specific nutrient management project that integrates laboratory data, basic statistics, and GIS tools to develop practical fertilization plans for real-world production fields.
1
80856
Lecture-Discussion
MDV
4:30PM -6:00PM
TR
337 National Soybean Res Ctr
Ersoz, E
Availability:
Open
Part of Term:
B
Date Range:
10/19/26-12/09/26
Credit:
3 hours
Section Title:
Int Modeling in Ag/BioSci - II
Section Info:
Integrative Modeling and Data Analytics in Agriculture-II This is a project based experiential learning course- where the trainees are anticipated to learn by doing a semester long Pick-Your-Own-Data Research Project. Instruction covers routinely used multivariate modeling and analytical techniques focusing on life sciences and agricultural applications in an integrated framework of computational, mathematical and statistical modeling. Discussion sessions are leveraged for examining applications from primary literature to understand, evaluate and critique choice of analytical methods used for answering research questions, and provide supplemental lectures as needed. Example topics covered are: Fundamental Multivariate modeling and deep learning with high dimensional data, classic dimension reduction techniques(discriminant analysis, K-means, canonical correlations), simple neural networks and regularization, autoencoders, CNNs, RNNs, foundational AI models and their workings, architectures and applications(LSTM, transformer, GNN, Deep-kernel models), Interpretibility techniques for NNs, ethics, safety and future of AI. Pre-requisite: AP level Programming, Calculus, Basic Statistics, and Fundamentals of Agricultural Science or equivalents or consent of the instructor.
1
79930
Lecture-Discussion
STA
4:30PM -6:00PM
TR
337 National Soybean Res Ctr
Ersoz, E
Availability:
Open
Part of Term:
A
Date Range:
08/24/26-10/16/26
Credit:
3 hours
Section Title:
Int. Modeling in Ag/BioSci- I
Section Info:
Integrative Modeling and Data Analytics in Agriculture-I This is a project based experiential learning course- where the trainees are anticipated to learn by doing a semester long Pick-Your-Own-Data Research Project. Instruction covers routinely used modeling and analytical techniques focusing on life sciences and agricultural applications in an integrated framework of computational, mathematical and statistical modeling. Discussion sessions are leveraged for examining applications from primary literature to understand, evaluate and critique choice of analytical methods used for answering research questions, and provide supplemental lectures as needed. Example topics Covered are: EDA, Fundamental regression and classification( linear, non-linear, logistic), curve fitting with OLS and MLE, missing data treatments & imputation, goodness-of-fit, regularization, ENSMBL methods, Variance components based modeling (Structural equation Models and Causal Inference) Pre-requisite: AP level Programming, Calculus, Basic Statistics, and Fundamentals of Agricultural Science or equivalents or consent of the instructor.
COURSE EXPLORER
Email: Course Explorer Feedback

OFFICE OF THE REGISTRAR | 901 W. Illinois Street, Urbana, Illinois 61801

Site developed by: Technology Services at Illinois | UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN
1102 Digital Computer Laboratory | MC-256 | Urbana, IL 61801 | phone 217-244-7000