Classes will be taught during the week, normally in the morning and/or afternoon and not just on weekends. The schedule will depend on the subjects that the student chooses to take in agreement with their advisor. In other words, within the list of subjects that we will be offering each semester, the student’s schedule will be set up according to the subjects that he or she will take during the semester.
The student needs to complete 26 credits (around 9 subjects). Normally the student tries to complete all the subjects in the first year, so that in the second year they are only involved with the dissertation. However, depending on the availability of time, the student can take the subjects, also using part of the second year to fulfill the credits.
Precision Agriculture – 3 credits (not mandatory): Introduction to precision agriculture: conceptualization. The Precision Agriculture cycle. Global Positioning Systems (Global Positioning System – GPS) and error handling. Direct and remote sensing. Monitoring the spatial variability of soil attributes. Geostatistics: concepts. Crop productivity maps. Software. Data acquisition and processing systems using portable microcomputers and dedicated programs. Geographic Information Systems – GIS: Localized application of inputs.
Algorithms and Computer Programming – 3 credits (not mandatory): Algorithm development. Basic and structured data types. Commands of a programming language. Program development methodology: structured and object-oriented programming. Modularity and abstraction. Development, implementation, debugging, testing.
Database – 3 credits (not mandatory): Database management systems. Database architecture. Database structure. Description and manipulation language. SQL language. Data models. Normalization. Entity/Relationship Modeling. Relational Modeling. Entity/Relationship to Relational Model Mapping. Description and application projects. Methodology for database analysis and design: information systems; data survey; modeling and development of database systems. Operational aspects of database management systems: transaction management; competition control; recovery after failure and security. Projects in applied computing.
Biochemical and Molecular Bases for Bioinformatics – 2 credits (not mandatory): Structure and function of Proteins, Carbohydrates, Lipids, DNA and RNA. Central Dogma of Molecular Biology. Application and techniques of metagenomics, genomics, transcriptomics and proteomics. Biological databases. Alignments of DNA and protein sequences, search for sequence similarity, structural motifs in protein sequences and phylogenetic analysis.
Biotechnology – 2 credits (not mandatory): Central Dogma of Molecular Biology, DNA, RNA, Protein; Purification of nucleic acids, extraction of plasmid DNA, restriction enzymes, plasmids, cloning, bacterial transformation; electrophoresis, PCR, qPCR, plant tissue culture, transgenics.
Computing Applied to Agriculture – 3 credits (compulsory): Principles of Agriculture. Precision agriculture: Computerized equipment. Analysis of georeferenced data. Radiometry. Computing applications in agriculture: forecasting systems, applied software. Internet for agriculture. Traceability. Computing principles. Basics of agricultural automation.
High Performance Computing – 3 credits (not mandatory): Introduction to parallel computing. Concurrency, scalability, locality, modularity and granularity. Introduction to parallel programming. Parallel programming models. Performance evaluation. Programming and debugging tools. Concepts and techniques related to distributed systems and applications. Client server and peer-to-peer model. Communication and synchronization in distributed systems. Distributed file systems. Tools for developing distributed applications. Security and fault tolerance in distributed systems.
Teaching-Oriented Internship – 1 credit (mandatory): Mandatory activity for all students who do not have proven experience in teaching at a higher level, of at least 15 hours. Participation as a teacher in a specific discipline, according to the subject matter, under the responsibility of a professor from the Postgraduate Program, aiming to provide students with effective training in teaching activities at graduation.
Fundamentals of Agriculture – 3 credits (not mandatory): Soil: physical, chemical and biological attributes limiting the development of plants. Sowing: times, density, spacing and varieties. Soil cover and cover crop management. Crop rotation. Conventional soil preparation system. Minimum cultivation. Direct planting system. Correction of soil acidity. Mineral nutrition. Fertilizing. Mechanical, chemical and biological cultural treatments. Harvest: harvesting techniques. Maturation and its influence on productivity and quality.
Fundamentals of Artificial Intelligence – 3 credits (not mandatory): Intelligent problem-solving techniques: search algorithms, heuristic search. Constraint satisfaction problems. Evolutionary computation. Knowledge-based systems in Knowledge Engineering and Ontologies. Treatment of uncertain and imprecise knowledge. Machine Learning.
Fundamentals of Soil Use and Management – 3 credits (not mandatory): Basic concepts of Soil Science. Edaphoclimatic characterization of tropical and subtropical environments. Soil management systems. Nutrient cycling in agrosystems. Soil quality. Soil management and climate change.
Geoprocessing – 3 credits (not mandatory): Introduction to geographic information systems and geoprocessing techniques. Use of geographic information systems to analyze agricultural spaces. Effects of spatial and temporal scales on structural factors and modifying agents of ecosystems. Analysis and interpretation of spatial patterns in agricultural and environmental variables. Georeferenced database design.
Non-Conventional Data Management – 2 credits (not mandatory): Object-oriented concepts. Object-oriented and object-relational databases. Object-oriented and object-relational database systems. Unconventional applications. Spatial database. Data Warehouse. Data and Metadata Management. Design and applications in unconventional data management. Projects in computing applied to agriculture.
Instrumentation – 3 credits (not mandatory): Introduction to instrumentation for agro-industrial equipment and environments. Sensors and transducers. Signal conditioning circuits. Microcontrollers for signal acquisition and processing. Signal processing techniques / error theory / statistics. Guided Project. Guidance for preparing an article related to the supervised project.
Computational Logic – 3 credits (not mandatory): Propositional logic. Syntax, semantics and inference algorithms. First Order Logic: syntax, semantics. Inference in First Order Logic: Unification, Resolution; completeness and consistency; Knowledge engineering in First Order Logic. Logic Programming and the PROLOG Language. Applications of LPO in the development of automatic planning systems and expert systems. Descriptive Logic. Inductive logic.
Scientific Research Methodology – 2 credits (mandatory): Computing and the classification of sciences. Scientific method. Research Methods. Current research styles in Computing. Preparation of a research paper. Writing of the monograph. Writing a scientific article. Computer tools for text editing and reference management.
Reasoning Methods under conditions of uncertainty – 3 credits (not mandatory): Probabilistic logic. Uncertain reasoning in rule-based systems. Bayesian networks. Inference in Bayesian networks: exact algorithms, approximate algorithms, distributed algorithms and algorithms for embedded systems. Applications of Bayesian networks in decision support systems and computer vision. Uncertain reasoning and temporal processes. Temporal probabilistic models: Markov chains. Hidden Markov models. Inference and learning in probabilistic temporal models. Applications of probabilistic temporal models. Fuzzy Logic. Fuzzy Reasoning and Learning.
Agricultural Data Mining – 4 credits (not mandatory): Artificial Intelligence Concepts. Definition of Data Mining. Objectives and case studies. Relationship of the data mining process with knowledge discovery in databases, statistics, visualization and distributed systems. Steps in the knowledge discovery process in databases. Descriptive and predictive Data Mining. Data mining tasks. Models, algorithms and tools for Data Mining. Data visualization techniques. Georeferenced data mining. Water and soil data mining. Concepts of Computational Intelligence and Spatial Data Mining.
Agricultural System Modeling – 3 credits (not mandatory): Concepts and Notation. Programming Paradigms: Object Orientation. UML Notation and Diagrams. Design Standards. Framework for developing Complex Agricultural Systems. Decision Support System Project in Agriculture: From Scientific Models to Final Software. Ontology-based simulation applied to soil, water and nutrient management. Modeling nutrient sensors for agricultural applications. Estimation of soil surface parameters through modeling. Modeling and Development of devices for estimating chlorophyll in vegetation. Classification and grouping algorithms in agricultural applications.
Dissertation Supervision I and II – 2 credits (mandatory): Mandatory activity, in each academic period, for every student in the dissertation development phase, defined by the official approval of their Advisor, who will evaluate the student’s performance in this activity. It is also mandatory, before the aforementioned officialization, for students who are not enrolled in any discipline; in this case, guidance and evaluation must be carried out by a Professor approved by the Course Coordinator.
Guided Research – 2 credits (not mandatory): Enable the student to carry out interdisciplinary scientific research work in parallel to the development of the dissertation, together with research groups included in the postgraduate program in applied computing.
Artificial Neural Networks Applied to Agriculture – 2 credits (not mandatory): Introduction, basic concepts of neural networks, perceptron algorithm, least-mean-square algorithm, error backpropagation algorithm, neural network simulators, applications and network modeling artificial neural networks in agriculture and the environment.
Seminars – 2 credits (mandatory): Lectures given by program professors and guests covering various topics in Computing and Agricultural Sciences, Multidisciplinary and Interdisciplinary Research and Integration of Research Projects.
Sensors and Actuators for Agriculture and the Environment – 3 credits (not mandatory): Sensors and characteristics. Conventional sensors. Sensors applied in agriculture. Actuation and power transmission mechanisms in systems. Study of prototypes or systems developed for agriculture and the environment. Other matters relevant to the development and construction of projects.
Simulation Applied to Plant Disease Management – 3 credits (not mandatory): Importance, history and objectives of epidemic simulation. Epidemiology. Agroecological Systems. Control measures for plant diseases. Integrated control of plant diseases.
Topics in Applied Computing – 2 credits (not mandatory): Varied content, according to the interest of the time, focused on topics related to Computing, Data Management and Applications in Agriculture.
Topics in Parallel and Distributed Computing – 2 credits (not mandatory): Tools for carrying out parallel and distributed programming. Discovery and management of distributed resources. Application scaling in distributed environments. Models and measures for evaluating the performance of parallel and distributed computing systems.
Topics in Software Engineering – 2 credits (not mandatory): Topics in Software Engineering varied, according to the interest of the time, focused on topics related to the Development of Information Systems Applied to Agriculture.