Texture Classification Algorithm Using RGB Characteristics of Soil Images

Soil texture has an important influence on agriculture, affecting crop selection, movement of nutrients and water, soil electrical conductivity and crop growth. Soil texture has traditionally been determined in the laboratory using pipette and hydrometer methods that require a considerable amount of time, labor, and expense. Recently, in–situ soil texture classification systems using optical diffuse reflectometry or mechanical resistance have been reported, especially for precision agriculture where more data is needed than in conventional agriculture. This paper is a part of overall research to develop a soil texture classification system using image processing. Application of image processing was motivated by simple traditional approaches such as visual inspection and the “hand–feel” method. In this paper, the potential of soil texture classification using RGB histograms was investigated. Seven sites representing major Korean paddy soil series were selected, 4–6 core samples up to a 50–cm intervals. For each segmented soil sample, four surface images were taken using a miniaturized CCD camera, and texture fractions were determined by the pipette method. Scatter plots showed linear patterns between silt content and histogram variables such as brightness (averaged pixel value), skewness, and difference between mode value and brightness (“mode–brightness”). When 5%–averaged silt content was linearly regressed with “mode–brightness”, squared correlation coefficient (R2), root mean square error of calibration (RMSEC), and root mean squared error of prediction (RMSEP) were 0.96, 2.2%, and 6.3%, respectively. When soils were classified using USDA soil texture classification, the laboratory method and the in–situ image processing method produced the same results for 48% of the samples