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Image Analysis in Soil Science

Magnus PerssonDepartment of Water Resources Engineering, Lund University,

Box 221 00 Lund, SwedenE-mail: magnus.persson@tvrl.lth.se

Abstract: In this article a review of different image analysis methods in the field of soil science is presented. The future potential in some methods is pointed out. In general these methods have proven to save a lot of labor intensive analysis work. However, a certain degree of subjectiveness is present in many methods, making comparison of results from different experts difficult.

IntroductionTransport of water and solutes through the soil poses one of the most seri-ous threats to our environment at present as well as in a possibly sustainable future. The soils and the vadose zone are constituents of a complex system of inorganic and organic compounds that cover almost the entire land sur-face of the earth. The unsaturated zone, situated between the soil surface and the groundwater table, plays an important role in the ecosystem as it is the base upon which the food chain is built. Plants take up nutrients and water from the unsaturated zone. Vegetation in turn, is consumed by ani-mals and human beings. Thus, harmful substances in the soil can pose a threat to human health. Another important role of the unsaturated zone is in the recharge of groundwater. Groundwater constitutes an important water supply component for, for instance, domestic, industrial, and agricul-tural use, and is often considered a clean water resource. However, pollut-ants from the soil surface can be transported down through the soil, thus contaminating the groundwater. A sustainable management of these is the fundament for a safe future food production, clean water supply, as well as a clean recreational environment. Image analysis can be defined as extraction of useful information from images by means of digital image processing techniques. In soil science, image analysis can be used to measure specific features of the soil or vegeta-tion. An image can be considered to be a large data matrix where each

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element (often called pixel) contains information about the brightness (in black and white images) or color (in color images) of a small area of the motif. Features of the image, like color or structures present, can be related to physical properties of the motif (or parts of it). Using image analysis it is possible to find patterns and boundaries, track objects, analyze colors, etc in an automated way making image analysis ideal for various applications like surveillance, robotics, astronomy, remote sensing and soil science. Images can be captured in several different ways. Nowadays digital cameras are used in most applications since they directly produce digital images that directly can be analyzed mathematically. The wavelength of the recorded light can be different, normally cameras record visible light (roughly 400–700 nm), but longer or shorter wavelength are also used (IR, UV, or even X-ray). The scales of the image can also vary; micro scale images can be obtained through microscopes while satellite images almost can cover entire continents. There is a great potential of using image analysis application in soil sci-ence. Both characteristics of the crops and the soil material itself can be measured using image analysis. The objective of this paper is to present a review of image analysis applications in the field of soil science. In this ar-ticle, I will focus on image analysis for determining soil characteristics since image analysis related to crops can be found elsewhere (e.g., Zheng and Moskal, 2009). For X-ray computed tomography imaging and other related methods see, e.g., Taina et al. (2008).

Image analysis in soil scienceIn soil science image analysis has become increasingly popular during the last decade. There are many interesting applications like: determining the size and shapes of macroporosity (e.g., Czachor and Lipiec, 2004), root mass and root length (Kimura et al., 1999), organic matter content (Chen et al., 2000), soil albedo (Post et al., 2000), water content (Persson, 2005b), and solute concentration (Forrer et al., 2002). Images are generally analyzed to determine either color of an object or to find and analyze shapes and patterns. There are many more types of analy-ses that can be done to extract useful information from images related to soil science. Some examples are Fourier transforms, fractal dimension analysis, texture analysis etc. One important process often used as one of the first steps in image analysis is segmentation. Segmentation means that the image is partitioned into segments containing multiple pixels corre-

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sponding to structural elements or other objects of interest. The purpose of segmentation is to reduce an image to information. One simple example is thresholding of a gray scale image to produce a binary image. Pixels with brightness above a certain threshold is given the value 1, all other pixels are given the value 0.

Some applicationsColor and color spacePhysically, the wavelength of light determines its color. The color of an object is determined both by the spectrum of the incident illumination and on the reflectance spectrum of the surface. A color model (color space) is an abstract mathematical model describing the way colors can be represented as tuples of numbers. Some examples are RGB (red, green, and blue), CMYK (cyan, magenta, yellow, and black), and HSV (hue, saturation, and value). In the RGB color space, which is the one used by most digital cam-eras and computer screens, all colors can be represented by mixing various amounts of red, green, and blue. In most applications each channels con-tains 8 bits of data 28 = 255. Thus 2553 = 16581375 different colors can be described, see Figure 1. Human perception of color is highly influenced by in-brain image analy-sis. Thus, an object of a specific, known color, appear to have the same color when viewed in different lights. However, an image of an object needs

Figure 1. Different colors in the RGB (red, green, and blue) color space.

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to be color corrected. Color consistency means that an image of an object should have the same color regardless of the ambient lighting conditions. Factors affecting the color of an object are; inhomogeneous illumination, color temperature, and image noise. Any image can be transferred to a com-mon norm by correcting for color temperature and inhomogeneous illumi-nation (see Persson, 2005a). Image noise, however, is random and cannot be corrected for. Using high end cameras and a powerful light source will in most cases reduce the image noise sufficiently. Different averaging filters can also be used.

Dye tracersDye tracers have been used for many years by soil scientists investigating the effects of soil heterogeneity as they allow visualization of spatial flow patterns (see, e.g., Flury and Flühler, 1995). This method has proven very useful for detecting preferential flow paths in the soil. Traditionally, image analysis of the dye photographs has only involved separation between stained and non-stained soil. However, using image analysis the dye con-centration can be estimated from soil color (e.g., Ewing and Horton, 1999; Forrer et al., 2002). The color as expressed in a specific color space of the corrected images can be related to dye concentration. In most cases, an empirical polynomial equation is adopted to model the relationship between RGB or HSV values and dye concentration (Ewing and Horton, 1999; Aeby et al., 2001; Persson et al., 2005). Other more complex models like neural networks have also been used (Persson, 2005a). The relationship between soil color and dye concentration is soil specific. One example is presented in Figure 2. The same fundamental principle of color consistency can also be used for determining other physical characteristics of soil material like organic mat-ter content (Chen et al., 2000), soil albedo (Post et al., 2000), or water con-tent (Persson, 2005b).

Pore space analysisThere are several ways to describing the shape related features of an object, e.g., formfactor, roundness, and elongation. Each of these is strictly math-ematically described. By defining limits for these parameters, different kinds of objects can be separated from each other. Image analysis of the soil pore space is normally done on small scale samples impregnated with a resin. Different types of resin have been used;

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usually the resin contains some sort of dye so that is stands out from the color of the soil grains. Using fluorescent resins and light sources with different wavelengths even more information can be extracted from the im-ages. After hardening the sample is cut into thin sections that are photo-graphed (e.g., Bouma et al., 1977; Protz et al., 1992; Ringrose-Voase, 1996). After segmentation the pore space can be analyzed to find the shape and size of pores between the grains. Furthermore, characteristics like connec-tivity of pores, types of soil material, and identification of earth worm channels etc can be determined.

Figure 2. The relationship between RGB values and dye concentration in three soil types.

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Some studies about automated classification of soils using image analysis have been presented. Marschallinger (1997) showed how image analysis could be used for classifying minerals in rock samples. Maragos et al. (2004) presented an automated system for estimation of the bioecological quality of soils using sophisticated image analysis. Generally these applications re-quire large computational efforts, but with the development of computers and image analysis software these application will likely become more used in the near future. Interesting attempts on relating micro scale characteristics of the pore network determined using image analysis to the transport of water and sol-utes. Pioneering work was carried out already by Bullock and Thomasson (1979) who compared characterization of macroporosity and water reten-tion data. Walker and Trudgill (1983) studied the relationship between im-age analysis of pore network and dispersivity.

Other applicationsImage analysis for determining root length and diameter has received a lot of attention. Previous analysis methods (manual inspection) were very labor intensive and several commercial image analysis software packages has been developed (e.g., Kirchhof, 1992; van der Weele, 2003; French et al., 2009).

Conclusion and DiscussionImage analysis and especially its application to soil science is still a young science. Most research within the field has been done during the last dec-ades. Some applications are already routinely used whereas many potential applications have received little attention yet. Especially interesting is auto-mated image analysis for soil classification and studies where pore scale soil properties are related to macroscopic features like water retention or soil fertility. Another area where I expect further development is the use of automated image analysis to study dynamic processes, e.g., solute transport or root growth. It should be noted that even if image analysis can be automated, it con-tains several steps which can be affected by subjectivity. Different opera-tors are likely to come to slightly different conclusions based on the same images. Thompson et al. (1992) discussed this problem and suggested some types of standardization. Unfortunately, very few of his suggestions have

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been implemented. Baveye et al. (2010) presented an interesting study where 13 experts analyzed a set of images. They found that experts rely on very different approaches to threshold images of soils, and that there is considerable observer influence associated with this thresholding. There-fore caution should be taken when results from different investigators are compared. Lastly I have to point out the great potential of using images and image analysis for educational purposes. Learning and understanding complicated processes is greatly enhanced when they can be visualized. Therefore image analysis applications are ideal in teaching or when communicating with the public.

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