2016
DOI: 10.1590/1807-1929/agriambi.v20n12p1051-1056
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Abstract: A B S T R A C TThe objective of this study was to analyze the relation between the moisture and the spectral response of the soil to generate prediction models. Samples with different moisture contents were prepared and photographed. The photographs were taken under homogeneous light condition and with previous correction for the white balance of the digital photograph camera. The images were processed for extraction of the median values in the Red, Green and Blue bands of the RGB color space; Hue, Saturation … Show more

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Cited by 34 publications
(8 citation statements)
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References 14 publications
(24 reference statements)
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“…Santos et al built a different linear regression model for each soil type and the resulting correlation coefficients varied between 0.8538 and 0.9506 [12]. Persson obtained better results with correlation coefficients ranging from 0.965 to 0.995 [11], yet, the investigated soils contained higher percentages of sand (above 40%) that increase their reflectance which is not the case with the present study ( Table 2). It is obvious that better prediction results are obtained when the regression models are trained with data of individual soil types.…”
contrasting
confidence: 61%
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“…Santos et al built a different linear regression model for each soil type and the resulting correlation coefficients varied between 0.8538 and 0.9506 [12]. Persson obtained better results with correlation coefficients ranging from 0.965 to 0.995 [11], yet, the investigated soils contained higher percentages of sand (above 40%) that increase their reflectance which is not the case with the present study ( Table 2). It is obvious that better prediction results are obtained when the regression models are trained with data of individual soil types.…”
contrasting
confidence: 61%
“…By comparing the results obtained for the different soil types (Tables 4 and 5), we notice that for type-4 (Eutric Leptosols) and for type-2 (Calcic Cambisols), the obtained R values are the highest and the obtained MSE values are the lowest. It is probably due to their colour which is lighter than the colour of other types (Table 1), an evidence assessed by [11].…”
Section: Results Interpretationmentioning
confidence: 99%
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“…The RGB color space model has the advantage of being able to reproduce most colors in a simple way. In addition, as mentioned above, because general electronic equipment (e.g., digital cameras, smartphones) used to acquire digital images adopts the RGB color space model, most existing studies [ 16 , 17 , 18 , 19 , 20 , 22 , 23 ] have obtained soil colors based on the RGB color space model.…”
Section: Methodsmentioning
confidence: 99%
“…Accordingly, many researchers are conducting research to obtain soil color using digital image processing and to analyze the correlation between soil color and soil property. Persson [ 16 ], Zanetti et al [ 17 ], Santos et al [ 18 ], Park [ 19 ], and Kim [ 20 ] reported the RGB color intensity of soil obtained from digital images taken in an indoor studio and analyzed the water content (or moisture content), and Zhu et al [ 21 ] analyzed the correlation between the gray color intensity and water content of soil obtained from black-and-white images. These studies revealed that the RGB color intensity and gray color intensity tend to decrease as the water content of the soil increases, and presented empirical equations capable of predicting the water content from the soil color.…”
Section: Introductionmentioning
confidence: 99%