The work is dedicated to the study of drone use in horticulture, focusing on an example of blackcurrant cultivation.The research aims to use drones to monitor vegetation in plantations and to maintain the technological environment of plants, using traditional agrotechnical field care methods.The concept offers mapping and instance segmentation followed by multi-label classification operations, taking into account the specifics of blackcurrant plantations.The mapping operation creates blackcurrant plantation maps from images taken by drones at low altitudes.This ensures the acquisition of highquality maps of large areas with the help of simple image photography cameras.Instance segmentation is intended for extracting singular leaf instances from mapped images, which are analyzed using classification methods to detect blackcurrant diseases, pest spread, nutrient and moisture deficiencies, and other plant vegetation-related parameters.Classification employs machine learning techniques and is specific to the cultivation of a particular plants -blackcurrants.The proposed technology, with appropriate adjustments, can also be applied to the vegetation monitoring of other horticultural plants.

Body measurement data are inherently inaccurate and quite error-prone due to manual measurement and data collection.In this study, professionally collected and selfcollected body measurement data were used to investigate to what extent potentially erroneous data can be identified during collection by utilizing the anthropologically given correlation of body measurements.The study specifically uses a dataset created within the framework of a project for made-to-measure pattern creation, consisting of data from 2053 female individuals with up to 52 recorded body measurements.Using linear regression, a method for validating the collected data is defined, wherein potentially inconsistent data are identified based on tolerance intervals.The tolerance intervals calculated within the study are specific to the particular application and the personal data used in the study.The outlined method is applicable to almost any set of manually collected body data in at least the triple-digit range, enabling the identification of probable data errors already during their collection.

Abstract4This study is devoted to process optimization for commercial sharing of e-vehicles.The model describes a system with one-way trips and relocations of e-vehicles between sectors by service personnel according to a dynamically compiled list of service trips.The model includes an algorithm for increasing the expected income, depending on the dynamically selected evehicle transfer.The implementation of the MIP (Mixed-Integer Programming) type algorithm pays particular attention to its performance, as optimization should be performed dynamically within few hours' intervals.The developed model has been validated for its practical application in Riga, Latvia.

The development of ICT facilitates replacing the traditional buying and selling processes with e-commerce solutions.If several customers are served concurrently, e.g. at the same time, the processes can interference each other causing risks for both the buyer and the seller.The paper offers a method to identify purchase/sale risks in simultaneous multicustomer service processes.First, an exact model of buyingselling processes is created and the conditions for the correct process execution are formulated.Then an analysis of all the possible scenarios, including the concurrently executed buyingselling scenarios, is performed using a symbolic execution of process descriptions.The obtained result allows both the buyer and the seller to identify the risks of an e-commerce solution.

This paper proposes a model-based testing approach by offering to use the data quality model (DQ-model) instead of the program's control flow graph as a testing model.The DQ-model contains definitions and conditions for data objects to consider the data object as correct.The study proposes to automatically generate a complete test set (CTS) using a DQmodel that allows all data quality conditions to be tested, resulting in a full coverage of DQ-model.In addition, the possibility to check the conformity of the data to be entered and already stored in the database is ensured.The proposed alternative approach changes the testing process: (1) CTS can be generated prior to software development; (2) CTS contains not only input data, but also database content required for complete testing of the system; (3) CTS generation from DQ-model provides values against which the system can be further tested.If the test results correspond to the values obtained during CTS generation, the system under test shall be considered to have been tested according to DQ-model.Otherwise, the user can verify the cause of the differences that may occur due incorrect software, as well as an inaccurate specification.

This paper discusses data quality checking during business process execution by using runtime verification.While runtime verification verifies the correctness of business process execution, data quality checks assure that particular process did not negatively impact the stored data.Both, runtime verification and data quality checks run in parallel with the base processes affecting them insignificantly.The proposed idea allows verifying (a) if the process was ended correctly as well as (b) whether the results of the correct process did not negatively impact the stored data in result of its modification caused by the specific process.The desired result will be achieved by use of domain specific languages that would describe runtime verification and data quality checks at every stage of business process execution.

The research discusses the issue how to describe data quality and what should be taken into account when developing an universal data quality management solution.The proposed approach is to create quality specifications for each kind of data objects and to make them executable.The specification can be executed step-by-step according to business process descriptions, ensuring the gradual accumulation of data in the database and data quality checking according to the specific use case.The described approach can be applied to check the completeness, accuracy, timeliness and consistency of accumulated data.

Steadily increasing complexity of software systems makes them difficult to configure and use without special IT knowledge.One of the solutions is to improve software systems making them "smarter", i.e. to supplement software systems with features of self-management, at least partially.This paper describes several software components known as smart technologies, which facilitate software use and maintenance.As to date smart technologies incorporate version updating, execution environment testing, self-testing, runtime verification and business process execution.The proposed approach has been successfully applied in several software projects.