Field conditions and the accuracy of visually determined Munsell soil color

Turk, Judith K.; Young, Rebecca A.

doi:10.1002/saj2.20023

Cited by 19 publications

(6 citation statements)

References 16 publications

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“…As previously discussed, observing soil colour using MSCB is highly influenced by observant expertise, colour vision, and experience. Earlier in [ 19 ], the authors discussed the accuracy of visually determining Munsell soil colour based on field and weather conditions. Many subsequent studies have therefore looked at and proposed alternative ways to measure soil colour accurately.…”

Section: Introductionmentioning

confidence: 99%

Determination of Munsell Soil Colour Using Smartphones

Nodi

Paul

Robinson

et al. 2023

Sensors

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Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only 9% for the top 5 predictions, the accuracy of the proposed method is 74%, which is significant.

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Section: Introductionmentioning

confidence: 99%

Determination of Munsell Soil Colour Using Smartphones

Nodi

Paul

Robinson

et al. 2023

Sensors

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“…The Munsell Plant Tissue Color Book (Munsell Color, X‐rite, Grand Rapids, MI) connects plant colors to visible color standards defined by hue, value, chroma (H V/C) color space. Connecting plant color to H V/C is a visual comparison process (ASTM International, 1989; ASTM International, 2018; Paglierani et al., 2020; Simon & Frost, 1987; Turk & Young, 2020) potentially subject to error or bias. When matching soil color to colors in the Munsell Soil Color Book (Munsell Color, X‐rite), 52% of soil scientists agreed on H V/C and 71% agreed on individual components H, V, and C (Turk & Young, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Connecting plant color to H V/C is a visual comparison process (ASTM International, 1989; ASTM International, 2018; Paglierani et al., 2020; Simon & Frost, 1987; Turk & Young, 2020) potentially subject to error or bias. When matching soil color to colors in the Munsell Soil Color Book (Munsell Color, X‐rite), 52% of soil scientists agreed on H V/C and 71% agreed on individual components H, V, and C (Turk & Young, 2020). People differ in capacity to perceive colors; color vision can be distorted by situational lighting (Reinecke et al., 2016), and there may be failure to adhere to visual color‐matching protocols, including using hue‐neutral backgrounds and north‐sky daylight (Voss, 1992).…”

Section: Introductionmentioning

confidence: 99%

Predicting Munsell color for turfgrass leaves

Berndt¹,

Gaussoin

2023

Crop Science

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Linking turfgrass color to hue, value, chroma (H V/C) in the Munsell Plant Tissue Color Book is a visual comparison process for specifying and communicating plant color. If subjectivity of visual comparison can be mitigated, then the accuracy of color matching may be improved. Research was conducted to develop an algorithm predicting H V/C from CIE-xyY color (xyY) in digital images of leaves of four turfgrasses. First, value-chroma (V/C) arrays for Munsell hue groups 5Y, 2.5GY, 5GY, 7.5GY, 10GY, and 2.5G were converted to xyY. Next, chromaticity (xy) plots from each array were fitted with 95% prediction bands. Then, xy points for leaves (N = 60) for each turfgrass were plotted versus prediction bands. The prediction interval containing the most leaf xy points specified leaf H. Pairing each V from each hue group (N = 98) with its corresponding Y regressing log 10 V on log 10 Y predicted leaf V from leaf Y (log 10 V = 0.0715 + 0.5303logY -0.0332logY 2 ). Pairing each C (N = 98) with its corresponding xy predicted leaf C from leaf xy (C = −11.6021 − 11.2908x + 48.0880y). Predicted H V/C for "Champion" and "TifEagle" hybrid bermudagrass [Cynodon dactylon (L.) Pers. var. dactylon × Cynodon transvaalensis Burtt-Davy] was 10GY 8/4 and 7.5GY 8/4 mirroring H V/C predicted from xyY using open-source algorithms in R and Python. The experimental algorithm predicted H V/C accurately, and objectively advancing the way turfgrass color is specified and communicated. Specifying turfgrass color in situ could be possible by interfacing the algorithm with smartphone technology.

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“…Instead, the rapid development of modern technologies and instrumental methods, such as UV VIS spectrophotometry, allow a more precise and quantitative approach to colour quality control [16,19,37]. They overcome some of the limitations of the Munsell method by removing the human 'judgement' from the analysis and using standard values, such as observer viewing angle and fixed lighting conditions to control the conditions of the measurement [8,9,16,23,[38][39][40]. For this reason, quantitative measures of colour (e.g., spectrophotometers) have seen apparent exponential growth worldwide and there are new applications of colour data in different fields [16,37].…”

Section: Introductionmentioning

confidence: 99%

Measuring Soil Colour to Estimate Soil Organic Carbon Using a Large-Scale Citizen Science-Based Approach

et al. 2021

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Rapid, low-cost methods for large-scale assessments of soil organic carbon (SOC) are essential for climate change mitigation. Our work explores the potential for citizen scientists to gather soil colour data as a cost-effective proxy of SOC instead of conventional lab analyses. The research took place during a 2-year period using topsoil data gathered by citizen scientists and scientists from urban parks in the UK and France. We evaluated the accuracy and consistency of colour identification by comparing “observed” Munsell soil colour estimates to “measured” colour derived from reflectance spectroscopy, and calibrated colour observations to ensure data robustness. Statistical relationships between carbon content obtained by loss on ignition (LOI) and (i) observed and (ii) measured soil colour were derived for SOC prediction using three colour components: hue, lightness, and chroma. Results demonstrate that although the spectrophotometer offers higher precision, there was a correlation between observed and measured colour for both scientists (R2 = 0.42; R2 = 0.26) and citizen scientists (R2 = 0.39; R2 = 0.19) for lightness and chroma, respectively. Foremost, a slightly stronger relationship was found for predicted SOC using the spectrophotometer (R2 = 0.69), and citizen scientists produced comparable results (R2 = 0.58), highlighting the potential of a large-scale citizen-based approach for SOC monitoring.

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Field conditions and the accuracy of visually determined Munsell soil color

Cited by 19 publications

References 16 publications

Determination of Munsell Soil Colour Using Smartphones

Determination of Munsell Soil Colour Using Smartphones

Predicting Munsell color for turfgrass leaves

Measuring Soil Colour to Estimate Soil Organic Carbon Using a Large-Scale Citizen Science-Based Approach

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