Using a Reference Color Plate to Correct Smartphone-Derived Soil Color Measurements with Different Smartphones Under Different Lighting Conditions

Soil color has long been used as an indicator for soil properties such as soil organic carbon and soil moisture. Recent developments in citizen science have seen the increased use of smartphone cameras for soil color measurements. However, there are high errors associated with this technique. Two major sources of errors are smartphone cameras and lighting conditions. These errors limit the applicability of this technique in citizen science. Existing correction methods for reducing these errors are either ineffective or too complicated or difficult to apply. There is also a lack of systematic analysis on how these correction methods can reduce errors. In this study, we tested the effectiveness of using a color plate as a reference to reduce the errors on color measurements due to the use of different smartphones and taking photos under different lighting conditions. Three types of objects were tested, including the squares on the color plate itself, the color chips in a Munsell soil color book, and soil samples. The results show that the raw values of color parameters showed different patterns of biases with different smartphones and lighting conditions. The calibration reduced the errors consistently for all smartphones under all lighting conditions for the color plate squares. For the Munsell book chips or the soil samples, the calibration did not always reduce the bias but it did reduce the variations in all color parameters among smartphones and lighting conditions and, therefore, improved the precision of color measurements.

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Publication Details

Journal
Soil Systems
Published
2025-08-26
DOI
https://doi.org/10.3390/soilsystems9030093
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Using a Reference Color Plate to Correct Smartphone-Derived Soil Color Measurements with Different Smartphones Under Different Lighting Conditions

Yulia Kupriyanovich, Masoud Goharrokhi, Alexander J. Koiter, David A. Lobb et al.
Soil Systems
Remote Sensing in Agriculture
article

Using a Reference Color Plate to Correct Smartphone-Derived Soil Color Measurements with Different Smartphones Under Different Lighting Conditions

Yulia Kupriyanovich, Masoud Goharrokhi, Alexander J. Koiter, David A. Lobb, Fangzhou Zheng, Sheng Li
article en

Abstract

Soil color has long been used as an indicator for soil properties such as soil organic carbon and soil moisture. Recent developments in citizen science have seen the increased use of smartphone cameras for soil color measurements. However, there are high errors associated with this technique. Two major sources of errors are smartphone cameras and lighting conditions. These errors limit the applicability of this technique in citizen science. Existing correction methods for reducing these errors are either ineffective or too complicated or difficult to apply. There is also a lack of systematic analysis on how these correction methods can reduce errors. In this study, we tested the effectiveness of using a color plate as a reference to reduce the errors on color measurements due to the use of different smartphones and taking photos under different lighting conditions. Three types of objects were tested, including the squares on the color plate itself, the color chips in a Munsell soil color book, and soil samples. The results show that the raw values of color parameters showed different patterns of biases with different smartphones and lighting conditions. The calibration reduced the errors consistently for all smartphones under all lighting conditions for the color plate squares. For the Munsell book chips or the soil samples, the calibration did not always reduce the bias but it did reduce the variations in all color parameters among smartphones and lighting conditions and, therefore, improved the precision of color measurements.

Soil SystemsVol. 9(3)
Agriculture and Agri-Food Canada (CA), University of Manitoba (CA), Brandon University (CA)
Agriculture and Agri-Food Canada
Openalex Percentile: Top 78%
Remote Sensing in Agriculture
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