Data Processing Technologies in Precision Agriculture
At the end of this course the student will have acquired knowledge, skills and abilities to:
- Understand a technical report where the information presented result from a statistical analysis and/or spatial modeling;
- Being able to synthesize information from a qualitative and quantitative dataset, in particular the interpretation and drawing of conclusions;
- Understand the bi-or multivariate relationships between variables of a dataset, and analyze redundancies and gaps of information;
- Distinguish the various sub-populations of a sample, and use the best tools for the generation of sub-sets of data;
- Generate estimated images of a continuous property locally sampled in the study area.
- Apply data analysis tools in R platform, including importing data, graphical view and the output of reports.
José António de Almeida, Sofia Verónica Trindade Barbosa
Weekly - 3
Total - 56
Lawal, Bayo (2014) Applied Statistical Methods in Agriculture, Health and Life Sciences, Springer.
Reis, Elizabeth (1997) Estatistica multivariada aplicada, Editora Sílabo.
Isaaks, E. H. & R. Mohan Srivastava (1989) An Introduction to Applied Geostatistics, Oxford University Press, New York, 561 p.
Rodriguez J. (1999) Ecología, Edições Pirámide. Caers, J (2011) Modeling Uncertainty in the Earth Sciences, Wiley-Blackwell.
Haining R. (2003) Spatial Data Analysis: Theory and Practice. Cambrige University Press.
Theoretical and practical sessions of 1 and 2 hours respectively: i) theoretical lectures with powerpoint ii) practical classes in computers. The practical classes are based on problem solving, taking as a starting point realistic datasets that reproduce some of the situations that future professionals will work.
The evaluation is preferably of continuous type but altrnatively can be made by classical exam. Two written tests for methods (representing 25% + 25% of the final grade), and a report made by groups of two students with a resolution of the practical problems worked in class practices (remaining 50 %) will be developed for the continuous assessment option. Alternatively, students have a final theoretical examination where the grade of the theoretical component tests can be also improved. Frequency is obtained by presence in 2/3 of the practical sessions.
Review of statistical analysis and probability theory. Colection of information, scale, spatial resolution. Display data with graphics. Categorical and continuous variables. Univariate and bivariate analysis. Center position, spread, asymmetry and kurtosis. Frequencies. Correlation and similitude. Probability. Uncertainty. Random variables. Probability distribution laws. Binomial and Poisson laws. Normal distribution law. Tests of hypothesis. Monte Carlo simulation. Regression. Generalized linear models. Spatio-temporal models. Trend curves. Multivariate analysis. Principal component analysis. Hierarchical and nonhierarchical (K-means) clustering methods. Analysis of variance (ANOVA). Geostatistics. Spatial analysis. Spatial covariance and variogram. Kriging estimation. Validation. Representation in GIS. Use of softwares R (data analysis), SGEMS (geostatistics) and ARCGIS (visualization of estimated images and initial data). Introduction to R platform.
Programs where the course is taught: