User-Technological Index of Precision Agriculture (UTIPA) is a comprehensive system based on mutual sharing of opinions and experience within community of people related to precision agriculture -farmers, technology suppliers and researchers. The main benefit of UTIPA is the possibility to use the calculated index level for particular technology (method) for precision agriculture and compare it to other technology with regards to different users, crops, regions etc. It evaluates the principle of a technology but does not take into account concrete products, brands or manufacturers. The index has significance for the presentation of the potential of precision agriculture, development planning and especially for the connection between technological innovativeness and usefulness for practice.The entire solution includes the methodology for the collection, processing and presentation of data and software and is available via a Web interface for all common device platforms. Anyone who has interest in precision agriculture and contributes their knowledge can use the collected data.

With emerging usage of positioned devices such as drones, cell phones or IoT, the amount of data that can be collected expands drastically. At any given time, there is usually at least one nearby device that has positioning capabilities. Smart phones, smart TVs, personal computers, or even cars contain localization features. These vast amounts of data require a lot of effort in analysis and understanding in order to be properly utilized, which is especially true for the field of agriculture, where proper analysis can yield tremendous improvements in terms of production. Current computer technologies offer plenty options for such analysis. However, not every agricultural subject has access to a mainframe with performance in petaflops to perform complicated analyses of such big data in a timely manner. The defined design patterns for creation of data offers potential for speeding up the analysis of ADS on personal computers. This article describes known and used creational patterns and compares their benefits regarding ADS and offers possible usage and improvements.

The aim of this paper is to describe one of the possible ways of making IT courses more interesting through collecting practical field data and a subsequent creation of databases. Since the establishment of a specialized peony garden near Prague in 2008, details of the origin of acquired plants and of their cultivation under local conditions have been continuously recorded. The data resulting from the peony research are used as support for our IT courses. They are used in practical modelling of a relational database and in creating student projects focused on responsive web designs. The peonies data make the IT courses more attractive and stimulating for students.Literature survey of the peony research publications and our own data obtained from the peony garden revealed some of the main problems in cultivating and identification of peonies. Herbaceous peonies are widespread perennials. Mostly "historical" varieties of Paeonia lactiflora are cultivated. However, these plants have long and weak stems, which are bent down by heavy flowers and must therefore be mechanically held up by stakes or support rings. By contrast, the new cultivars have solid stems, such as the hybrid herbaceous peonies and intersectional hybrids (Itoh hybrids) and do not require as much labour but are not commonly cultivated. Our research has discovered that most peony cultivars in home gardens have not been properly identified. Great emphasis has, therefore, been placed on the correct identification of peony cultivars.