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Registration Number

0219U004625, 0119U000493 , R & D reports

Title

System analysis of anomalies in images and their application to data analysis of remote sensing of agricultural lands

Head

Skoriukova Yanina Germanivna,

Registration Date

16-09-2019

Organization

Vinnitsa National Technical University

Description

A review of known approaches to solving the problem of finding such anomalies has been carried out and what information technologies should be used at the preprocessing, machine deep learning stages and which typical problems should be eliminated during this, taking into account the specifics of the subject area. The main criteria that should be taken into account to solve the problem are highlighted: the duration of the calculations, the accuracy (minimum error) of the model training, the proximity of the average area of clusters to the given one, subject to a number of restrictions. An expression of the integral criterion for taking into account these criteria and approaches to the choice of their weights are proposed. An algorithm has been developed for applying the proposed approaches and techniques for applying the known methods of machine depth learning and clustering. A real example of the application of this algorithm is given and its efficiency is demonstrated for cases where the most significant (with weighing 0.5) criterion is the duration of the calculations and when the proximity of the average area of clusters to the given one. The proposed set of approaches and techniques for systematic analysis of the size of a fragment of an aerial photography image of the agricultural lands will improve the accuracy and speed of searching for anomalies in them by machine deep learning methods and, in general, will allow for more efficient and timely detection of various plant diseases, weeds, pests, and the like.

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Довгополюк Сергій Олександрович
Дратований Михайло Володимирович
Мокін Віталій Борисович