Identification of negative factors affecting crop growth using orthogonal transformations

This article focuses on methods for analyzing aerospace images. Aerospace images are widely used to study agricultural crops belonging to the A. I. Baraev Scientific-Research and Production Center for Grain Farming in Northern Kazakhstan. The main objective of the research is to identify the key factors negatively affecting crop growth using aerospace images and to develop algorithms and methodologies to highlight them. To solve this problem, modern methods such as the spectral reflectance coefficient (SRC), NDVI (Normalized Difference Vegetation Index), clustering techniques, and orthogonal transformations are applied. Additionally, the study addresses the segmentation of text areas into sub-areas, for which algorithms and software tools are being developed to identify features that describe text differences. These methods are being refined through in-depth analysis of experimental data and may be used in the future to detect weeds, pests, and other factors that hinder crop growth. The challenges of processing signal images are of great interest to specialists in various scientific fields, such as physics, aeronautics, meteorology, and forensic science. The uniqueness of aerospace images lies in their broad potential applications in agricultural production. Modern image processing systems play a crucial role in solving applied problems, making a significant contribution to improving agricultural efficiency. Moreover, the proper processing and interpretation of data obtained from aerospace images enable the efficient use of water and land resources in agricultural areas. This approach helps determine crop conditions, soil moisture, and other environmental indicators, providing a basis for informed decision-making. The use of aerospace technologies opens up new opportunities for automating agricultural processes and developing innovative management systems.