Identification of factors that negatively affect the growth of agricultural crops by methods of orthogonal transformations - Yessenova, Moldir; Abdikerimova, Gulzira; Adilova, Aknur; Yerzhanova, Akbota; Kakabayev, Nurbol; Ayazbaev, Talgatbek; Sattybaeva, Zeinigul; Ospanova, Tleugaisha (Eastern-European Journal of Enterprise Technologies, 2022)
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