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Agricultural and Biological Engineering

Agricultural and Biological Engineering

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Graduate Courses in Agricultural and Biological Engineering

ABE 5038: Fundamentals and Applications of Biosensors
Introduction to biosensors, design and performance analysis. Fundamental application of biosensor theory will be demonstrated, including recognition, transduction, signal acquisition, and post-processing/data analysis.

Prerequisite: At least senior status in engineering and background in biology including biomolecules.

Credits: 3
Semester Offered: Spring
Instructor: Dr. Jose Reyes

ABE 5152: Advanced Fluid Power Circuits and Control
Engineering analysis, design, and experimentation of electro-hydraulic circuits and systems. Design of hydraulic circuits, fluid power system components, hydraulic actuator analysis, servo, and proportional valve performance, and electro-hydraulic control theory and applications.

Prerequisite: Senior level undergraduate standing with EGM3400, EGN3353C completed. Graduate Students are encouraged but not required to take EML5311 concurrently.

Credits: 3
Semester Offered: Spring (Even Years)
Instructor: Dr. Tom Burks

ABE 5442: Bioprocess Engineering
Engineering principles, processes, and techniques for using biological agents for the production of chemicals, food, biofuels, and pharmaceuticals, and waste treatment.

Credits: 3
Semester Offered: Fall
Instructor: Dr. Pratap Pullammanappilli

ABE 5643C: Biological Systems Modeling
Introduction to concepts and methods of process-based modeling of biological systems; physiological, populational, and agricultural applications.

Credits: 3
Semester Offered: Fall
Instructors: Dr. Greg Kiker, Dr. Rafael Muñoz-Carpena and Dr. Ray Huffaker

ABE 5646: Agricultural and Biological Systems Simulation
Basic concepts of systems analysis, modeling, and computer simulation of dynamic biological and agricultural systems. Methods for working with models, including sensitivity analysis, parameter estimation, and model evaluation. Applications of models in agricultural and biological systems.

Prerequisite: MAC 2312, STA 3032 or STA 4322

Credits: 3
Semester Offered: Spring
Instructor: TBD

ABE 5707C: Agricultural Waste Management
The course will introduce biological processes employed for treatment of agricultural, municipal, and agro-processing residues and wastes. It will include biological, physical and chemical principles related to characterization and analysis of pollutants, biological and physico-chemical transformation of pollutants, and engineering principles for design and operation of treatment systems. Specifically, the course will focus on analysis and design of lagoons, anaerobic digesters and composting systems. Field trips to operating systems will reinforce the concepts taught during lecture.

Prerequisite: 4 or higher classification courses in Biological, Chemical or Environmental Engineering

Credits: 3
Semester Offered: Spring
Instructor: Dr. Pratap Pullammanappilli

ABE 5815C: Food and Bioprocess Engineering Design Design and analysis of fermentation, thermal, freezing, evaporation, dehydration; and mechanical, chemical and phase separation processes as governed by principles of conservation of mass and energy, reaction kinetics and rheology of food and biological materials.

Credits: 4
Semester Offered: Fall
Instructor: Ziynet Boz

ABE 5936: Writing Grant Proposals for Scholarships and Fellowships
Provides incoming graduate students in the ABE Department an introduction to acquiring scholarships, fellowships, internships, and graduate assistantships from federal funding agencies. Students will be introduced to funding sources and opportunities, provided guidelines for proposal writing, and prepare a mock proposal for instructor and peer review.

Prerequisite: ENC3246 or equivalent technical writing course, and graduate status in the Agricultural and Biological Engineering Department.

Credits: 2
Semester Offered: Fall
Instructor: Dr. Henry Medeiros

ABE 6005: Applied Control for Automation and Robotics
Introduction to industrial controls, programmable logic controllers, and manipulator application programming in agricultural and biological engineering. Kinematics, dynamics, and control strategies for serial link manipulators in agricultural applications.

Prereq: EML 5311, equivalent, or consent

Credits: 3
Semester Offered: Spring (Odd Years)
Instructor: Dr. Tom Burks

ABE 6017: Seminar on Stochastic Modeling in Ecology and Hydrology
This course takes a problem-based approach to introduce stochastic modeling in the context of ecology and hydrology. The students will be asked to study selected papers in detail, through reading and in-class discussion, such that they understand how to set up the problems and derive some results and mathematical expressions reported therein.

Prereq: Graduate standing. Basic calculus and college-level probability course.

Credits: 3
Semester Offered: Fall
Instructor: Dr. Rachata Muneepeerakul

ABE 6031: Instrumentation in Agricultural Engineering Research
Principles and application of measuring instruments and devices for obtaining experimental data in agricultural engineering research.

Credits: 3
Semester Offered: Fall
Instructor: Dr. Tom Burks

ABE 6035: Advanced Remote Sensing: Science and Sensors
Develop an understanding of remote sensing theory, systems and applications using information obtained from the visible/near infrared, thermal infrared and microwave regions of the EM spectrum.

Prerequisite: MAP 2302 or the equivalent

Credits: 3
Semester Offered: Spring
Instructor: Dr. Jasmeet Judge

ABE 6037C: Remote Sensing in Hydrology
Develops a practical understanding of remote sensing applications to hydrology using observations in different regions of the EM spectrum. Seminar-style with an emphasis on literature review and presentation.

Prerequisite: ABE 6035

Credits: 3
Semester Offered: Fall (Even Years)
Instructor: Dr. Jasmeet Judge

ABE 6252: Advanced Soil and Water Management Engineering
Physical and mathematical analysis of problems in infiltration, drainage, and groundwater hydraulics.

Credits: 3
Semester Offered: Spring (Even Years)
Instructor: Dr. Richard Scholtz

ABE 6254: Simulation of Agricultural Watershed Systems
Characterization and simulation of agricultural watershed systems including land and channel phase hydrologic processes and pollutant transport processes. Investigation of the structure and capabilities of current agricultural watershed computer models.

Prerequisite: CWR 4111 and working knowledge of FORTRAN

Credits: 3
Semester Offered: Fall (Odd Years)
Instructors: Dr. Rafael Muñoz-Carpena and Dr. Greg Kiker

ABE 6265: Vadose Zone Water and Solute Transport Modeling
Unsaturated zone modeling of water flow and solute transport processes. Comparative analysis of alternative mechanistic modeling approaches of different complexity.

Prerequisite: Recommended basic use of high-level computer language or numerical computing environment (i.e., Matlab, Mathematica, etc.) that allows the student to test algorithms and read existing modeling source code

Credits: 3
Semester Offered: Summer A (Even Years)
Instructor: Dr. Rafael Muñoz-Carpena

ABE 6266: Nanotechnology in Water Research
Applications of environmental nanotechnology to water quality control. Fate and transport of nanomaterials in hydrologic pathways.

Prerequisite: Basic knowledge of hydrology, environmental engineering, and water chemistry

Credits: 3
Semester Offered: Fall
Instructor: Dr. Bin Gao

ABE 6615: Advanced Heat and Mass Transfer in Biological Systems
Analytical and numerical technique solutions to problems of heat and mass transfer in biological systems. Emphasis on nonhomogeneous, irregularly shaped products with respiration and transpiration.

Co-Requisites: COP 2271 and ABE3612C

Credits: 3
Semester Offered: Spring
Instructor: Dr. William Pelletier

ABE 6644: Agricultural Decision Systems
Decision Support Systems are programs or tools to organize and synthesize information to support management decision making. They are commonly used to assist with managing agricultural systems, to help solve natural resource management issues, or to assist in policy advising. As such, they are sometimes closely related to or applied within agricultural extension and farmer education and can be both computerized and human powered. They are also commonly used by the private sector to provide information to producers and other stakeholders. With the advancement of remote sensing, Big Date, and the Internet of Things, decision support systems are providing new and challenging opportunities to provide timely and accurate information to a broad range of clients.

Credits: 3
Semester Offered: Spring (Odd Years)
Instructors: Dr. Gerrit Hoogenboom

ABE 6645C: Computer Simulation of Crop Growth and Management Responses
Teaches the background of computer models for the dynamic simulation of crop growth, development, and yield, and soil and plant water, nutrient, and carbon dynamics, and the application of models to real-world problems. The course is based on a systems analysis approach using DSSAT as a platform.

Recommended that students have a basic understanding of crop and soil science

Credits: 3
Semester Offered: Summer C
Instructors: Dr. Gerrit Hoogenboom

ABE 6649C: Advanced Biosystems Modeling
This course serves as an advanced graduate class for continued modeling of biological processes and systems. It is the second and required course of the Biological Modeling Certificate offered by ABE.

The course extends and deepens the curriculum of ABE5643C in that it covers more advanced modeling topics such as: (1) hands-on experience and confidence in formulating, solving (analytically and numerically with R programming), (2) interpreting the output of dynamic biological models; (3) object-oriented design and programming, cellular automata and agent-based model development, (4) High Performance Computing and Global Sensitivity and Uncertainty Analysis towards subjects of specific interest.

Prerequisite: This course requires ABE 5643C as a prerequisite or by an admission from one of the instructors. 

Credits: 3
Semester Offered: Spring
Instructors: Dr. Greg Kiker, Dr. Rafael Muñoz-Carpena and Dr. Ray Huffaker

ABE 6840: Data Diagnostics: Detecting and Characterizing Deterministic Structure in Time Series Data
Application of nonlinear time series analysis to detect, characterize, and model deterministic structure in real-world time series data. Topics include signal processing, phase space reconstruction, surrogate data testing, causal network analysis, and AI recurrent neural networks.

Credits: 3
Semester Offered: Fall
Instructor: Dr. Ray Huffaker

ABE 6905: Individual Work in Agricultural and Biological Engineering

Credits: 1-4 (Max 6)

ABE 6910: Supervised Research

Credits: 1-5 (Max 5)

ABE 6931: Seminar
Preparation and presentation of reports on specialized aspects of research in agricultural engineering and agricultural operations management.

Credits: 1
Semesters Offered: Fall, Spring
Instructor: Dr. Richard Scholtz

ABE 6933: Applied Case Study Data Analysis

Credits: 3
Semester Offered: Spring

ABE 6933/4932: Controlled Environment Production Systems Design
This course provides a concentrated study of design criteria, analysis techniques, and new technologies for plant production under controlled environments. Classes focus on various controlled environment systems, including high tunnels, greenhouses, and vertical farming systems. Students will learn technical skills of controlled environment systems, understand how agricultural structures and system components affect plant growing environment, and how to select materials and equipment to design a controlled environment agriculture system. Students will develop technical skills for energy-conscious designs of heating, ventilation, and air conditioning (HVAC) for load calculation, equipment selection, and system design and evaluation. Operation and management techniques including CO2 enrichment, irrigation and fertigation, and sensing and control are introduced to students in the course.

Pre-Requisites/Co-Requisites:
MAC1147 Precalculus Algebra and Trigonometry and 3 credits of physics

Credits: 3
Semester Offered: Spring
Instructor: Dr. Ying Zhang

ABE6933: Foundations of Probability and Math Statistics
This course is a fast-paced introduction to calculus-based probability and statistics aimed at graduate students in engineering and quantitative life sciences. It covers the essentials of STA5325 and STA5328 in a single semester making use of calculus and basic scientific computing to gain understanding about fundamental results and methods of probability and statistics.

Knowledge of calculus of multiple variables. Experience reading and writing simple computer programs in a scripting language (ideally, in R); basic knowledge of scientific computing (e.g., ABE 5643C for ABE students). Undergraduate statistics or a recent first graduate statistical methods class (such as ALS5932 or STA6166) is preferred.

Credits: 3
Semester Offered: Spring
Instructor: Dr. Nikolay Bliznyuk

ABE 6933/4932: Applications of Life Cycle Assessment in Biological Engineering
This is a special topics course that will explore the topic of life-cycle assessment (LCA) in relation to biological engineering design. The course will be project based with students working in teams to identify a current challenge within water-energy-food systems and develop an engineering solution to address this challenge. A key component of this course will be applying LCA as a tool for informing the design process and evaluating the environmental impacts of engineered products and processes. Through the course, students will be introduced to additional concepts including circular economy, mass and energy balances, and life-cycle costing. Students will examine these concepts through case studies and apply them in a team design project.

Prereq: MAC1147 Precalculus Algebra and Trigonometry

Credits: 3
Semester Offered: Spring
Instructor: Drs. Ana Martin-Ryals and Ziynet Boz

ABE 6933: Logistics of Agricultural Food Chains
This course covers logistic strategy and concepts for agricultural food chains, and the techniques and tools needed to improve supply chain efficiency and solve logistics problems.

Prereq: Basic skill of Math and Statistics, knowledge of farming operations

Credits: 3
Semester Offered: Spring
Instructor: Dr. Fedro Zazueta

ABE 6933/4932: Advanced Robotic Systems Design

This course provides graduate and undergraduate students foundational skills for the design, implementation, and
performance assessment of agricultural robotic systems.

Prereq: ABE 4171C or equivalent for undergraduate students or ABE 6005 or equivalent for graduate students. Both prerequisite courses can be taken concurrently with this course.

Credits: 2
Semesters Offered: Spring
Instructor: Dr. Henry Medeiros

STA6703 (formerly ABE6933): Statistical Machine Learning
This course focuses on the methodology and application of tools of statistical (machine) learning. In contrast with courses with similar names offered by Computer Science (CS) and Industrial Engineering (IE), it emphasizes statistical approaches to machine learning. The course prioritizes application and the intuition behind statistical methods rather than formal derivations and justification of the procedures.

Prereq: Recent first graduate statistical methods class (such as ALS5932 or STA6166). Experience reading and writing simple computer programs in a scripting language (ideally, in R). Basic undergraduate quantitative training (calculus and basic matrix/linear algebra). Stats/biostats grad students with stronger math and stats background are welcome.

Credits: 3
Semester Offered: Fall
Instructor: Dr. Nikolay Bliznyuk

ABE6933: Fundamentals & Applications of Solar Energy

Credits: 3
Semester Offered: Spring

ABE 6933: Spatial Statistics
This course is an introduction to spatial statistics, with a focus on methods that are relevant for public health applications, as well as earth and environmental sciences. It is primarily intended for two audiences: (i) statisticians who want to get exposed to methods and applications and (ii) researchers from other elds with some training in statistics that routinely work with spatial data and would like to learn appropriate statistical models and methods.

Sufficient background: First-year required masters-level coursework in statistics at UF.

Minimal sufficient background: a solid graduate course in regression (such as STA6207) with exposure to matrix notation; a solid course in inference at the level of STA5328; basic scientific and statistical computing skills; motivation (particularly, to pick up R)

Ideal background: in addition to the minimum background above, proficiency with matrix algebra and basic numerical linear algebra (STA6329); statistical computing using R; exposure to linear mixed models, generalized linear models and generalized linear mixed models; masters-level sequence in probability and inference STA 6326-6327; interest (and/or need) to apply methods learned in this course in your research work.

Credits: 3
Semester Offered: Fall

ABE 6940: Supervised Teaching

Credits: 1-5 (Max 5)
Semesters Offered: Fall, Spring

ABE 6971: Research for Master's Thesis

Credits: 1-15
Semesters Offered: Fall, Spring, Summer

ABE 6974: Non-Thesis Project

Credits: 1-15

ABE 6986: Applied Mathematics in Agriculture and Life Sciences
Mathematical methods, including regression analysis, graphical techniques, and analytical and numerical solution of ordinary and partial differential equations, relevant to engineering in agriculture and the related sciences.

Pre-requisites/Co-requisites: MAP2302: Elementary Differential Equations or equivalent.

Credits: 3
Semester Offered: Spring
Instructor: Dr. Richard Scholtz

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Graduate Courses in Agricultural Operations Management

AOM 5334C: Agricultural Chemical Application Technology
Equipment and methods used to apply pesticides in agriculture. Emphasis on techniques to avoid misapplication and pesticide drift.

Credits: 3
Semester Offered: Fall
Instructor: Dr. Wonsuk "Daniel" Lee

AOM 5435: Advanced Precision Agriculture
Principles and applications of technologies supporting precision farming and natural resource data management planning. Global positioning systems (GPS), geographic information systems (GIS), variable rate technologies (VRT), data layering of independent variables, automated guidance, Internet information access, computer software management.

Prereq: Graduate student standing or permission of instructor

Credits: 3
Semester Offered: Fall (Even Years)
Instructor: Dr. Wonsuk "Daniel" Lee

AOM 5456: Applied Methods in SmartAg Systems

Design, analysis, and evaluation of SmartAg methods for applications in production agriculture, biological and food engineering, forestry, land, and water resources. Students will learn hardware and software concepts used in SmartAg applications with real-world examples (e.g., UAV’s, irrigation, controlled environments for plant and animals, crop modeling).

Credits: 3
Semester Offered: Spring (Even Years)
Instructor: Dr. J. Adam Watson

AOM6061/4060: Agri-Food Systems Innovation
Students explore the role of innovation in food systems from a reverse chain perspective. Students will gain knowledge of the food system framework from a multi-level (i.e., individual, organizational, etc.) perspective, identify current, innovative business and technological practices, as well as present and think critically about future trends in food.

Pre-requisites and Co-requisites: AOM or ABE or PKG, junior standing or by instructor approval

Credits: 3
Semester Offered: Spring
Instructor: Dr. J. Adam Watson

AOM 6735: Irrigation Principles and Management

Designed to teach graduate students about irrigation and gain skills to evaluate an irrigation system, identify parts of a system, and develop
an irrigation schedule based on system characteristics. This course is designed for nonengineers although quantitative ability will be required for calculations and analysis. Prerequisites Students must be proficient in Microsoft Excel and Word. Students should be able to use equation functions and graphing functions in Excel. It is recommended that students have basic understanding of hydrology, unit conversions, and algebra.

Pre-requisites and Co-requisites: Students must be proficient in Microsoft Excel and Word. Students should be able to use equation functions and graphing functions in Excel. It is recommended that students have basic understanding of hydrology, unit conversions, and algebra.

Credits: 3
Semester Offered: Fall - Not currently being offered

AOM 6905: Individual Work in Agricultural Operations Management

Credits: 1-6 (Max 6)
Semester Offered: Fall

AOM 6932: Controlled Environment Plant Production

This course covers foundational information on the principles of controlled environment plant production. Students are introduced to concepts describing the interactions between plants and their microenvironments created by different production systems and climate control strategies. Engineering aspects of environmental control will be discussed. Current technologies and practices for indoor plant production are reviewed. Students are presented with current trends in the controlled environment industry, and are asked to identify costs, develop budgets, and make decisions that impact profitability, output, and marketing methods in plant-production supply chains.

Credits: 3
Semesters Offered: Fall
Instructor: Dr. Celina Gómez, Dr. Ying Zhang, and Dr. Adam Watson

AOM 6736: Principles and Issues in Environmental Hydrology
This is a basic course in Environmental Hydrology intended for Agricultural and Natural Resource Managers. The first half of the course covers scientific principles of the hydrologic cycle while the second half investigates case studies of current water quality and water management issues.

Prereq: This course will use simple and intermediate algebraic equations and trigonometry. Sophomore level chemistry and physics, as well as mathematics through pre-calculus, are recommended. Significant experience with Microsoft Excel or similar spreadsheets is required in select assignments.

Credits: 3
Semester Offered: Fall (Online)
Instructor: Dr. Greg Kiker

AOM 6932: Agri-Food Systems Innovation
This course is open to undergraduates and graduates alike and requires students to explore the role of innovation in food systems from a reverse chain perspective. Students will gain knowledge of the food system framework from a multi-level (i.e., global, national, and regional/local) perspective, identify current, innovative business and technological practices, as well as present and think critically about future trends in food.

Credits: 3
Semester Offered: Spring
Instructor: J. Adam Watson

AOM 6932: Advanced Intro to Biofuel

Semester Offered: Summer A
Instructor: Dr. Pratap Pullammanappallil

AOM 4932/6932: Agricultural Intensification: Tradeoffs or Synergies with the Environment and Livelihoods

This interdisciplinary course is designed to teach students about the principles of sustainable agricultural intensification (SAI) and to explore the challenges to achieve SAI. We will begin with the history, science and impact of agricultural intensification, including the Green Revolution that doubled global food supplies between 1970 and 1995. We explore the effects of agricultural intensification on the environment (water quality, greenhouse gases, biodiversity), and human livelihoods (income, food security, nutrition). Though the focus is on developing countries the course will include temperate and regional comparisons for a broader understanding of the global food production system.

Credits: 2
Semester Offered: Spring - Not currently being offered

AOM 6932: Sustainable Agricultural Intensification

Credits: 2
Semester Offered: Spring - Not currently being offered

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Graduate Courses in Packaging

PKG 5006: Advanced Principles of Packaging
Modern lab instruments and procedures employed for packaging used to solve problems in the packaging industry.

Prerequisite: Chemistry, physics, or biology

Credits: 3
Semester Offered: Fall
Instructor: Dr. William Pelletier

PKG 6100: Advanced Computer Tools for Packaging
Label design, bar code technology, spreadsheets, visual basic programming, 3D package design, and distribution efficiency analysis.

Credits: 3
Semester Offered: Spring
Instructor: Dr. Bruce Welt

PKG 6905: Individual Work in Packaging

Credits: 1-6 (Max 6)

PKG 6932: Advanced Food Packaging

Credits: 3
Semester Offered: Fall
Instructor: Dr. Bruce Welt

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Other Departmental Courses

AGG 5607: Communicating in Academia - Guide for Graduate Students
This course is designed to teach graduate students about academic writing, specifically focused on research proposals, theses, dissertations, manuscripts, grant proposals, and CVs. The course was developed to teach students about aspects of academic writing that are not normally part of graduate curriculum but are necessary to succeed

Credits: 3
Semester Offered: Spring, Distance Education with chat sessions
Instructor: Dr. Heather Enloe and Dr. Rachata Muneepeerakul

STA 6348 Bayesian Analysis for Machine Learning and Uncertainty Quantification
This course emphasizes Bayesian methodology for modeling, inference and prediction using hierarchical/multilevel models, with particular emphasis on computation (Monte Carlo and its flavors) and applications (inference and prediction in models for regression and classification, potentially with various types of statistical dependence) as intended for Master’s and doctoral students in data sciences and engineering.

Credits: 3
Semester Offered: Fall (Even Years)
Instructor: Dr. Nikolay Bliznyuk

 

STA 6703 Statistical Machine Learning 

Methodology and application of tools of statistical (machine) learning targeted at graduate students in engineering, applied statistics/biostatistics and quantitative life sciences. Statistical approaches to machine learning are emphasized in order to expand on and complement existing courses in engineering. Application and the intuition behind statistical methods rather than formal derivations and full mathematical justification of the procedures are prioritized.

Credits: 3
Semester Offered: Fall
Instructor: Dr. Nikolay Bliznyuk

 

STA 6709 Spatial Statistics & Hierarchical Modeling for Dependent Data 
Methodology and application spatial statistics targeted at graduate students in data sciences, engineering and quantitative life sciences. Topics include models for point-referenced/geostatistical, areal and spatial point process patterns data, spatial regression, hierarchical Bayesian framework for spatial (generalized) linear models; advanced topics: spatio-temporal processes, multivariate random fields, approximate Bayesian inference using INLA.

Credits: 3
Semester Offered: Fall (Odd years)
Instructor: Dr. Nikolay Bliznyuk