Calibration methods for measuring the color of moist soils with digital cameras

Кириллова, Н. П.; Zhang, Y.; Hartemink, Alfred E.; Zhulidova, D. A.; Artemyeva, Z. S.; Khomyakov, D. M.

doi:10.1016/j.catena.2021.105274

Cited by 16 publications

(5 citation statements)

References 20 publications

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“…In the future, the results can be obtained using other instruments, including in the field. Although modern instruments for soil colour evaluation (spectrophotometers, spectroradiometers, and photocolourimeters) are used [10,13], digital devices (cameras) [5,8] and flatbed scanners are more accessible to researchers [6,45,46]. In this case, a constant artificial light source is used, or the photograph is taken in natural light, but using calibration scales.…”

Section: Resultsmentioning

confidence: 99%

“…The colour of objects is key for the detection of boundary ramparts according to these data. The soil colour is a useful integral indicator, as it is determined by the complex of the results of pedogenesis, such as organic matter, carbonates, clay minerals, quartz oxides of iron, manganese, and water-soluble salts [5][6][7][8][9]. The use of colour as a deciphering feature was based on ground level and laboratory research.…”

Section: Introductionmentioning

confidence: 99%

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Differentiating Soils from Arable and Fallow Land Using Spectrometry

2021

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In this study, we analyse soil system responses—in particular, the colours of soils resulting from different types of land use (arable land and residential areas) caused by the construction of an ancient boundary rampart near a multilayer monument dating from between prehistory and the Middle Ages within the boundaries of the Bosporus Kingdom (Eastern Crimea)—in an area of modern and ancient (4th–2nd centuries BC) land use (Northwestern Crimea). These differences are of interest because they offer the chance to decipher different types of ancient land use and systems of land surveying, incorporating data from colour aerial photographs obtained with the help of unmanned aerial vehicles. Soil samples displaying different types of anthropogenic transformation were taken from the ancient boundary ramparts and adjacent land. The soil colour coordinates in the CIE L*a*b system were measured with the help of an AvaSpec-2048 spectrometer. Differences in colour coordinates were analysed using analysis of variance (NPMANOVA) based on the permutation test, the Kruskal–Wallis test, and the Mann–Whitney test, corrected according to multiple comparisons carried out as per the Bonferroni method. The results of this statistical analysis show that there are statistically significant differences in soil colour coordinates between samples collected on the ramparts and under various other types of land use. These differences are more pronounced in the samples characterised by the agrogenic transformation of soil. This makes it possible to use remote sensing data to detect traces of ancient boundary ramparts, even if the ramparts are partially destroyed by ploughing.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differentiating Soils from Arable and Fallow Land Using Spectrometry

2021

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“…Computer vision and soil images can be used to estimate soil moisture [2][3][4][5]. Under the same soil texture and fertility, the Gray value of soil surface image [6], soil apparent electrical conductivity (ECa) [7], color parameters [8,9], and soil mechanical resistance (MR) [10] can be used for data fusion to rapid estimate surface soil moisture.…”

Section: Introductionmentioning

confidence: 99%

Shadow removal method of soil surface image based on GAN used for estimation of farmland soil moisture content

Meng

Yang

Wang

et al. 2023

Meas. Sci. Technol.

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It is important to obtain soil moisture content (SMC) in farmland, and soil surface images can be used to rapidly estimate SMC. The objective of this study was to propose a shadow removal algorithm to eliminate the effect of shadows in soil surface images，so as to improve the accuracy of SMC estimation. The structure of the proposed Soil Shadow Generative Adversarial Networks (SS GAN) was a circulating network, which is an unsupervised method and does not require paired shadow image sets for network training. Four loss functions were defined for the network to effectively remove shadows and ensure texture detail and color consistency. This method is compared with traditional methods, supervised and unsupervised deep learning techniques by comparative experiments. Evaluations were made from visual and quantitative comparisons. Visually, the best shadow removal method was proved, it almost has no shadow boundaries or shadow areas visible for samples. The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) were used to quantitatively compare shadow removal images with real non-shadow images. The PSNR and SSIM of SS GAN were 28.46 and 0.95 respectively, which are superior to other methods, indicating that the images processed by SS GAN were closer to the real non-shadow images. Field experiments results shown that SS GAN has excellent shadow removal performance in the self-developed vehicle-mounted detection system. In order to verify the improvement effect of shadow removal image on SMC estimation accuracy, further field test was conducted to estimate SMC. Compared with SMC estimation results before and after shadow removal, R2 increased from 0.69 to 0.76, and Root Mean Square Error (RMSE)decreased from 1.39 to 0.94%. The results show that the proposed method can effectively remove the shadow of soil image and improve the accuracy of SMC estimation in farmland.

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“…Drawing upon the principle of the remote sensing technique, the image-based methods have been proposed and increasingly applied to measure soil moisture content [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ].The image-based technique commonly employs an ordinary digital camera to capture soil sample, and the moisture content is derived from soil color using the pre-established calibration curve. It is obvious that the calibration relationship between soil moisture content and its color is the key for employing the image-based technique.…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al [ 31 ] developed a water content estimation equation using soil color and suggested a calibration method for field application of this equation to predict soil water content with unmanned aerial vehicle (UAV) images. Besides, Kirillova et al [ 32 ] investigated calibration methods for measuring the color of moist soils with digital cameras and showed that a decrease in soil moisture leads to a significant increase in lightness and yellowness.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the Relationship between the Loess Moisture and Image Grayscale Value

Liu

Wang

Zheng

et al. 2021

Sensors

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This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments.

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Calibration methods for measuring the color of moist soils with digital cameras

Cited by 16 publications

References 20 publications

Differentiating Soils from Arable and Fallow Land Using Spectrometry

Differentiating Soils from Arable and Fallow Land Using Spectrometry

Shadow removal method of soil surface image based on GAN used for estimation of farmland soil moisture content

Characterization of the Relationship between the Loess Moisture and Image Grayscale Value

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