This article is devoted to a set of important areas of research: the analysis of formal representations and verification of pests and pathogens affecting crops using spectral brightness coefficients (SBR) for the period from 2021 to 2023. The database contains about 10,000 records covering the growing season, types of diseases and pests, as well as their growth phases in a real coordinate system. The work uses machine learning techniques including logistic regression, extreme gradient boosting (XGBoost), and Vanilla convolutional neural network (CNN) to analyze spectral data and classify the presence of pests and diseases in satellite images. The main goal of the work is to optimize and improve the quality of agricultural productivity through early detection and accurate classification of pests and diseases in the agricultural sector. The results of the study can be applied in the development of innovative agricultural systems that will increase yields, reduce the cost of pest and disease control, and optimize production processes. The conclusions of this work can be used both as scientific and practical recommendations for agricultural enterprises and organizations and for the development of new technologies and programs for automating agricultural processes. The use of machine learning techniques and spectral data analysis promises significant breakthroughs in the agricultural sector, helping to improve the efficiency, sustainability, and quality of crop production.

This paper focuses on aerospace image analysis methods. Aerospace images are considered for the study of agricultural crops of northern Kazakhstan belonging to the A. I. Barayev Research and Production Center for Grain Farming. The main goal of the research is the development and implementation of algorithms that make it possible to detect and highlight on aerospace images the factors that negatively affect the growth of crops over the growing seasons. To resolve the problem, the spectral brightness coefficient (SBC), NDVI, clustering, orthogonal transformations are used. Special attention was paid to the development of software tools for selecting characteristics that describe texture differences to segment texture regions into sub-regions. That is, the issue of the applicability of sets of textural features and orthogonal transformations for the analysis of experimental data to identify characteristic areas on aerospace images that can be associated with weeds, pests, etc. in the future was investigated. The questions of signal image processing remain the focus of attention of different specialists. The images act both as a result and as a research object in physics, astronautics, meteorology, forensic medicine and many other areas of science and technology. Furthermore, image processing systems are currently being used to resolve many applied problems. A program has been implemented in the MATLAB environment that allows performing spectral transformations of six types: 1) cosine; 2) Hadamard of order 2n; 3) Hadamard of order n=p+1, p≡3 (mod4); 4) Haar; 5) slant; 6) Daubechies 4. Analysis of the data obtained revealed the features of changes in the reflectivity of cultivated crops and weeds in certain periods of the growing season. The data obtained are of great importance for the validation of remote space observations using aerospace images

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.

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.

Automated processing of aerospace information makes it possible to effectively solve scientific and applied problems in cartography, ecology, oceanology, exploration and development of minerals, agriculture and forestry, and many other areas. At the same time, the main way to extract information is to decipher images, which are the main carrier of information about the area. Aerospace images are a combination of natural texture regions and man-made objects. This article discusses methods for analyzing texture images. The main tasks of the analysis of texture areas include the selection and formation of features that describe texture differences, the selection and segmentation of texture areas, the classification of texture areas, and the identification of an object by texture. Depending on the features of the texture areas of the images used, segmentation methods based on area analysis can be divided into statistical, structural, fractal, spectral, and combined methods. The article discusses textural features for the analysis of texture images, and defines informative textural features to identify negative factors for crop growth. To solve the tasks, textural features are used. Much attention is paid to the development of software tools that allow to highlight the features that describe the differences in textures for the segmentation of texture areas. This approach is universal and has great potential on the studied aerospace image to identify objects and boundaries of regions with different properties using clustering based on images of the same surface area taken in different vegetation periods. That is, the question of the applicability of sets of texture features and other parameters for the analysis of experimental data is being investigated. Автоматизована обробка аерокосмічної інформації дозволяє ефективно вирішувати наукові та прикладні завдання картографії, екології, океанології, розвідки та розробки корисних копалин, сільського та лісового господарства та багатьох інших галузей. При цьому основним способом отримання є розшифровка зображень, що є основним носієм інформації про місцевість. Аерокосмічні зображення є комбінацією областей з природною текстурою та штучних об'єктів. У статті розглядаються методи аналізу текстурних зображень. До основних завдань аналізу текстурних областей відносяться виділення та формування ознак, що описують текстурні відмінності, виділення та сегментація текстурних областей, класифікація текстурних областей, ідентифікація об'єкта за текстурою. Залежно від особливостей текстурних областей використовуваних зображень методи сегментації, що ґрунтуються на аналізі площ, можна розділити на статистичні, структурні, фрактальні, спектральні та комбіновані методи. У статті розглядаються текстурні ознаки аналізу текстурних зображень, і навіть визначаються інформативні текстурні ознаки виявлення негативних чинників зростання сільськогосподарських культур. Для вирішення поставлених завдань використовують фактурні особливості. Велика увага приділяється розробці програмних засобів, що дозволяють виділяти ознаки, що описують відмінності текстур сегментації текстурних областей. Цей підхід універсальний і має великі можливості на аерокосмічному знімку, що вивчається, для виявлення об'єктів і меж регіонів з різними властивостями за допомогою кластеризації на основі знімків однієї і тієї ж ділянки поверхні, зроблених в різні вегетаційні періоди. Тобто досліджується питання про застосування наборів текстурних ознак та інших параметрів для аналізу експериментальних даних.

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.