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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Moldir Yessenova
L. N. Gumilyov Eurasian National University
Computer Science & IT Electrical & Electronics Engineering

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The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops Moldir Yessenova; Gulzira Abdikerimova; Talgatbek Ayazbaev; Gulden Murzabekova; Aisulu Ismailova; Zhanar Beldeubayeva; Aliya Ainagulova; Ayagoz Mukhanova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1669-1679

Abstract

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth’s surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture. 
Features of growth of agricultural crops and factors negatively affecting their growth Moldir Yessenova; Gulzira Abdikerimova; Zhanna B. Sadirmekova; Natalya Glazyrina; Saltanat Adikanova; Adilbek Tanirbergenov; Mukhamedrakhimov Karipola; Galiya Mukhamedrakhimova
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp625-632

Abstract

This article is about methods of analyzing aerial images. Images from Planet.com for crops in North Kazakhstan owned by the Center for Cereal Production and Research. A.I. Barayev. The main goal of the research work is to develop and implement algorithms that allow identifying and distinguishing factors in aerial photographs that adversely affect the growth of plants during the growing season. Spectral brightness coefficient (SBC), normalized difference vegetation index (NDVI), textural features, clustering, and integral transformations are used to solve the problem. Particular attention has been paid to the development of software tools for selecting features that describe textural differences to divide texture regions into subregions. That is weeds, and pests in aerial images. The application of a set of textural features and orthogonal transformations to the analysis of experimental data is explored to identify regions of potentially correlated features in the future. The analysis of the received data made it possible to determine the characteristics of changes in the reflective capacity of agricultural plants and weeds in certain stages of the growing season. The obtained information is of great importance for confirming the observations from space remote from the aerial images.
Application of informative textural Law’s masks methods for processing space images Moldir Yessenova; Gulzira Abdikerimova; Gulden Murzabekova; Kakabayev Nurbol; Natalya Glazyrina; Saltanat Adikanova; Nurgul Uzakkyzy; Zhanna B. Sadirmekova; Rozamgul Niyazova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4557-4566

Abstract

Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.
Applying textural Law’s masks to images using machine learning Gulzira Abdikerimova; Moldir Yessenova; Akbota Yerzhanova; Zhanat Manbetova; Gulden Murzabekova; Dinara Kaibassova; Roza Bekbayeva; Madina Aldashova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5569-5575

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 machine-learning 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 high-resolution images in the case of small samples using the example of problems of segmentation and classification of the nature of damage to crops.
Co-Authors Adilbek Tanirbergenov Aisulu Ismailova Akbota Yerzhanova Aliya Ainagulova Ayagoz Mukhanova Dinara Kaibassova Galiya Mukhamedrakhimova Gulden Murzabekova Gulden Murzabekova Gulzira Abdikerimova Kakabayev Nurbol Madina Aldashova Mukhamedrakhimov Karipola Natalya Glazyrina Nurgul Uzakkyzy Roza Bekbayeva Rozamgul Niyazova Saltanat Adikanova Talgatbek Ayazbaev Zhanar Beldeubayeva Zhanat Manbetova Zhanna B. Sadirmekova Zhanna B. Sadirmekova