Abstract:
Currently, artificial neural networks are experiencing a rebirth, which is
primarily due to the increase in the computing power of modern computers
and the emergence of very large training data sets available in global
networks. The article considers Laws texture masks as weights for a machinelearning algorithm for clustering aerospace images. The use of Laws texture
masks in machine learning can help in the analysis of the textural
characteristics of objects in the image, which are further identified as pockets
of weeds. When solving problems in applied areas, in particular in the field of
agriculture, there are often problems associated with small sample sizes of
images obtained from aerospace and unmanned aerial vehicles and
insufficient quality of the source material for training. This determines the
relevance of research and development of new methods and algorithms for
classifying crop damage. The purpose of the work is to use the method of
texture masks of Laws in machine learning for automated processing of highresolution images in the case of small samples using the example of problems
of segmentation and classification of the nature of damage to crops.
