Informatization is primary characteristic of current agriculture stage. Precision Agriculture is destination of new technologies and principles in agriculture using digital information.We consider data analysis as an aspect of Precision Agriculture and introduce UChooBoost applied to classification problem in agriculture.UChooBoost is a supervised learning ensemble-based algorithm for extended data, based on bootstrap technique. UChoo classifier is used as Weak Learner. Combining hypotheses by new weighted majority voting developed for extended results expression allows UChooBoost to achieve better performance level.

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Recently, as an alternative to chromate protective layers at aluminum and its alloys, worldwide are used nanoscale adhesive coatings, obtained from hexafluorozirconic and hexafluorotitanic acids. The present study is devoted to the technology of development of these coatings.The process of deposition of titanium, zirconium-containing coatings at aluminum alloy 5556 is developed. In the process of performing the work, the basic patterns of coating formation were identified. The solution composition and process parameters were optimized, and the physicochemical properties were investigated. It is shown that passivation of aluminum alloy 5556 in a titanium,zirconium-containing solution increases its corrosion resistance to pitting corrosion. These coatings can replace chromate coatings because of good ability to resist corrosion.It was found that the thickness of titanium-, zirconium-containing coatings is about 100 nm.

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

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