Assessing the soil color by traditional method and a smartphone: a comparison

Autores

DOI:

https://doi.org/10.22267/rcia.213801.146

Palavras-chave:

Pedometrics, Soil analysis pro, Munsell soil color chart, Proximal sensing, Android, Digital camera

Resumo

Based on the hypothesis that there is a high agreement between pedologists and a smartphone application in the assessment of soil color; the objective was to compare the perceptions of pedologists and an application in obtaining the color of an Argissolo [Lixisol] (A, E and B horizons).  Ten aggregates of each horizon were collected. In a single day, under the same lighting conditions, three pedologists described the color components (hue, value, and chroma) of each aggregate (dry and moist soil) using the Munsell soil color chart. Each one of the ten aggregates, from each horizon, was photographed (dry and moist soil sequence) using the camera of a Motorola Moto G4 Plus smartphone. The distance of the camera to the aggregates was 25 ± 5 cm. Also, each aggregate was placed on a white sheet of A4 size paper (background). The application used was Soil Analysis Pro. The percentage of agreement between pedologists and application was obtained concerning hue, value, and chroma. The data were subjected to analysis of variance, in a completely randomized design, with ten replicates. Action Stat® software was used for statistical analysis. It was concluded that the agreement between pedologists and the smartphone application was medium for hue and chroma and low for value. For the dry soil condition, there is a high agreement between pedologists and the smartphone application, especially in the perception of hue and chroma. Thus, the smartphone application has the potential to be used in routine descriptions of soil color.

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Pongnumkul, S.; Chaovalit, P.; Surasvadi, N. (2015). Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research. Journal of Sensors, 18. doi: https://doi.org/10.1155/2015/195308

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Santos, H.G.; Jacomine, P.K.T.; Anjos, L.H.C.; Oliveira, V.A.; Lumbreras, J.F.; Coelho, M.R., Almeida, J.A.; Araujo Filho, J.C.; Oliveira, J.B.; Cunha, T.J.F. (2018). Sistema brasileiro de classificação de solos. 5º ed. Brasília: Embrapa. 590 p.

Skorka, O.; Dileepan J. (2011). Toward a digital camera to rival the human eye. Journal of Electronic Imaging, 20(3). doi: https://doi.org/10.1117/1.3611015

Stiglitz, R.; Mikhailova, E.; Post, C.; Schlautman, M.; Sharp, J. (2016). Evaluation of an inexpensive sensor to measure soil color. Computers and Electronics in Agriculture, 121, pp. 141-148. doi: https://doi.org/10.1016/j.compag.2015.11.014

Stiglitz, R.; Mikhailova, E.; Post, C.; Schlautman, M.; Sharp, J. (2020). Using an inexpensive color sensor for rapid assessment of organic carbon. Geoderma, 286, 98-103.doi: https://doi.org/10.1016/j.geoderma.2016.10.027

Stiglitz, R.; Mikhailova, E.; Post, C.; Schlautman, M.; Sharp, J.; Pargas, R.; Glover, B., Mooney, J. (2017). Soil color sensor data collection using a GPS-enabled smartphone application. Geoderma, 296, 108-114. doi: https://doi.org/10.1016/j.geoderma.2017.02.018

Simon, T.; Zhnag, Y.; Hartemink, A.E.; Huang, J.; Water, C.; Yost, J.L. (2020). Predicting the color of sandy soils from Wisconsin, USA. Geoderma, v.361. doi: https://doi.org/10.1016/j.geoderma. 2019.114039

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Publicado

2021-05-08

Como Citar

Raulino, G. de C., Oliveira, L. de S., Nascimento, I. V., da Silva, C. P., Lobato, M. G., Alencar, T. L., Toma, R. S., da Silva, F. G., & Mota, J. C. (2021). Assessing the soil color by traditional method and a smartphone: a comparison. Revista De Ciencias Agrícolas, 38(1), 75–85. https://doi.org/10.22267/rcia.213801.146