TY - JOUR
T1 - From sensor data to Munsell color system
T2 - Machine learning algorithm applied to tropical soil color classification via Nix™ Pro sensor
AU - Mancini, Marcelo
AU - Weindorf, David C.
AU - Monteiro, Maria Eduarda Carvalho
AU - de Faria, Álvaro José Gomes
AU - dos Santos Teixeira, Anita Fernanda
AU - de Lima, Wellington
AU - de Lima, Francielle Roberta Dias
AU - Dijair, Thaís Santos Branco
AU - Marques, Francisco D.Auria
AU - Ribeiro, Diego
AU - Silva, Sérgio Henrique Godinho
AU - Chakraborty, Somsubhra
AU - Curi, Nilton
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Soil color has historically drawn humans’ attention, although its definition is somewhat subjective. It is correlated with several soil attributes and it allows for inferences about several soil aspects. New proximal sensors, such as the Nix™ Pro color sensor, can determine soil color values, but its correlation with the widely used Munsell soil color chart (MSCC) has yet to be investigated. This work aimed to train machine learning models using the Random Forest (RF) algorithm to predict each notation of MSCC chips from data extracted by the Nix™ Pro sensor, test the model's accuracy by evaluating whether it can identify MSCC chips using a brand-new and a dirty MSCC, and compare model predictions with soil color classifications made by the human eye. Additionally, MSCC data obtained via Nix™ was compared to Munsell renotation data to assess the color detection accuracy of the sensor. Prediction models were calibrated by scanning every MSCC chip (437 in total) in triplicate. All validation samples were excluded from model calibration. Accuracy of the predictions of MSCC notation reached overall accuracy and Kappa index values of 0.93 for the brand-new MSCC and of 0.70 for the dirty MSCC. Soil color classification by human eye had little agreement with the predicted MSCC notation, as expected due to the variable conditions affecting soil color conventional determination in the field. Color difference was calculated by the Euclidian distance (ΔE*ab) between three color stimuli in the CIELAB color space. The mean ΔE*ab between Nix™-provided data and renotation data was 2.9, demonstrating high color detection accuracy. The Nix™ Pro color sensor allows for assessment of accurate color data. When applied together with machine learning algorithms, Nix™ Pro provides a reliable determination of soil color classification equivalent to MSCC in an easily reproducible, rapid, inexpensive and non-subjective way.
AB - Soil color has historically drawn humans’ attention, although its definition is somewhat subjective. It is correlated with several soil attributes and it allows for inferences about several soil aspects. New proximal sensors, such as the Nix™ Pro color sensor, can determine soil color values, but its correlation with the widely used Munsell soil color chart (MSCC) has yet to be investigated. This work aimed to train machine learning models using the Random Forest (RF) algorithm to predict each notation of MSCC chips from data extracted by the Nix™ Pro sensor, test the model's accuracy by evaluating whether it can identify MSCC chips using a brand-new and a dirty MSCC, and compare model predictions with soil color classifications made by the human eye. Additionally, MSCC data obtained via Nix™ was compared to Munsell renotation data to assess the color detection accuracy of the sensor. Prediction models were calibrated by scanning every MSCC chip (437 in total) in triplicate. All validation samples were excluded from model calibration. Accuracy of the predictions of MSCC notation reached overall accuracy and Kappa index values of 0.93 for the brand-new MSCC and of 0.70 for the dirty MSCC. Soil color classification by human eye had little agreement with the predicted MSCC notation, as expected due to the variable conditions affecting soil color conventional determination in the field. Color difference was calculated by the Euclidian distance (ΔE*ab) between three color stimuli in the CIELAB color space. The mean ΔE*ab between Nix™-provided data and renotation data was 2.9, demonstrating high color detection accuracy. The Nix™ Pro color sensor allows for assessment of accurate color data. When applied together with machine learning algorithms, Nix™ Pro provides a reliable determination of soil color classification equivalent to MSCC in an easily reproducible, rapid, inexpensive and non-subjective way.
KW - Color system
KW - Pedology
KW - Prediction models
KW - Proximal sensors
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85085583916&partnerID=8YFLogxK
U2 - 10.1016/j.geoderma.2020.114471
DO - 10.1016/j.geoderma.2020.114471
M3 - Article
AN - SCOPUS:85085583916
SN - 0016-7061
VL - 375
JO - Geoderma
JF - Geoderma
M1 - 114471
ER -