Abstract
We present an automated system that we have developed for estimation of the bioecological quality of soils using various image analysis methodologies. Its goal is to analyze soilsection images, extract features related to their micromorphology, and relate the visual features to various degrees of soil fertility inferred from biochemical characteristics of the soil. The image methodologies used range from low-level image processing tasks, such as nonlinear enhancement, multiscale analysis, geometric feature detection, and size distributions, to object-oriented analysis, such as segmentation, region texture, and shape analysis.
Highlights
The goal of this research work is the automated estimation of the bioecological quality of soils using image processing and computer vision techniques
Our approach has been the development of an automated system that will recognize the characteristics relevant to the soil quality by computer processing of soilsection images and extraction of suitable visual features
We list the most important of such problems which we have investigated for detecting characteristics and extracting information from soilsection images: (1) enhancement of images; (2) feature detection; (3) multiscale image analysis; (4) statistical size distributions; (5) segmentation into homogeneous regions; (6) texture analysis; (7) shape analysis; and (8) correspondence of the features extracted from analyzing the soilsection images with the fertility grade of the soil inferred from its biochemical characteristics
Summary
The goal of this research work is the automated estimation of the bioecological quality of soils using image processing and computer vision techniques. Its final goals are double-fold: (1) quantification of the micromorphology of the soil via analysis of soilsection images and (2) correspondence of the extracted visual information with the classification of soil into various fertility degrees inferred from measurements performed biochemically on the soil samples. The tools and methodologies that we have used for solving the above image analysis problems (1)–(7) include the following: (i) nonlinear geometric multiscale lattice-based image operators (of the morphological and fuzzy type) for multiscale image simplification and enhancement, extracting presegmentation features, size distributions, and region-based segmentation; (ii) nonlinear partial differential equations (PDEs) for isotropic modeling and implementing various multiscale evolution and visual detection tasks; (iii) fractals for quantifying texture and shape analysis from the viewpoint of geometrical complexity; (iv) modulation models for texture modeling from the viewpoint of instantaneous spatial frequency and amplitude components; and (v) topological and curvature-based methods for region shape analysis. Methods of fuzzy logic and neural networks were investigated for the symbolic description and automated adaptation of the correspondence between the soilsection images and the bioecological quality of soil
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