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

Gabriela de Castro Raulino; Lucas de Sousa Oliveira; Icaro Vasconcelos Nascimento; Cillas Pollicarto da Silva; Márcio Godofrêdo Lobato; Thiago Leite Alencar; Raul Shiso Toma; Francisca Gleiciane da Silva; Jaedson Cláudio Mota

doi:10.22267/rcia.213801.146

Autores

Gabriela de Castro Raulino Universidade Federal do Ceará https://orcid.org/0000-0001-6583-7002
Lucas de Sousa Oliveira Universidade Federal do Ceará
Icaro Vasconcelos Nascimento Universidade Federal do Ceará https://orcid.org/0000-0001-5222-3589
Cillas Pollicarto da Silva Instituto Federal de Educação, Ciência e Tecnologia do Amazonas https://orcid.org/0000-0001-8183-2025
Márcio Godofrêdo Lobato Universidade Federal do Cariri- Campus Crato https://orcid.org/0000-0001-6694-6366
Thiago Leite Alencar Instituto Federal de Educação, Ciência e Tecnologia do Piauí https://orcid.org/0000-0002-0420-2000
Raul Shiso Toma https://orcid.org/0000-0001-5585-6832
Francisca Gleiciane da Silva Universidade Federal do Ceará https://orcid.org/0000-0002-9216-3151
Jaedson Cláudio Mota Universidade Federal do Ceará https://orcid.org/0000-0002-1261-9754

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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Referências

Broken Oak studios. (2017). Soil Analysis Pro. Recovered from https://cutt.ly/xvV6eEt

FAO. (2014). World reference base for soil resources. Rome: Food and Agriculture Organization (FAO). 203 p.

Fan, Z.; Herrick, J.E.; Saltzman, R.; Matteis, C.; Yudina, A.; Nocella, N.; Crawford, E.; Parker, R. Van Zee, J. (2017). Measurement of soil color: a comparison between smartphone camera and the Munsell color charts. Soil Science Society of America Journal, 81, 1139-1146. doi: 10.2136/sssaj2017.01.0009

Han, P.; Dong, D.; Zhao, X.; Jiao, L.; Lang, Y. (2016). A smartphone-based soil color sensor: for soil type classification. Computers and Electronics in Agriculture, 123, 232-241. doi: https://doi.org/10.1016/j.compag.2016.02.024

Jahn, R.; Blume, H. P.; Asio, V. B., Spaargaren; O; Schad, P. (2006). Guidelines for soil description. 8º ed. Rome: Food and Agriculture Organization (FAO). 97 p.

Kirillova, N.P.; Grauer-gray, J.; Harteminkb A.E.; Sileovaa, T.M.; Artemyevac, Z.S.; Burova, E. K. (2018). New perspectives to use Munsell color charts with electronic devices. Computers and Electronics in Agriculture, 155, 378-385. doi: https://doi.org/10.1016/j.compag.2018.10.028

Marqués-Mateu, Á.; Moreno-Ramón, H.; Balasch, S.; Ibáñez-Asensio, S. (2018). Quantifying the uncertainty of soil colour measBroken Oak studios. (2017). Soil Analysis Pro. Recovered from https://cutt.ly/xvV6eEt

FAO. (2014). World reference base for soil resources. Rome: Food and Agriculture Organization (FAO). 203 p.

Fan, Z.; Herrick, J.E.; Saltzman, R.; Matteis, C.; Yudina, A.; Nocella, N.; Crawford, E.; Parker, R. Van Zee, J. (2017). Measurement of soil color: a comparison between smartphone camera and the Munsell color charts. Soil Science Society of America Journal, 81, 1139-1146. doi: 10.2136/sssaj2017.01.0009

Han, P.; Dong, D.; Zhao, X.; Jiao, L.; Lang, Y. (2016). A smartphone-based soil color sensor: for soil type classification. Computers and Electronics in Agriculture, 123, 232-241. doi: https://doi.org/10.1016/j.compag.2016.02.024

Jahn, R.; Blume, H. P.; Asio, V. B., Spaargaren; O; Schad, P. (2006). Guidelines for soil description. 8º ed. Rome: Food and Agriculture Organization (FAO). 97 p.

Kirillova, N.P.; Grauer-gray, J.; Harteminkb A.E.; Sileovaa, T.M.; Artemyevac, Z.S.; Burova, E. K. (2018). New perspectives to use Munsell color charts with electronic devices. Computers and Electronics in Agriculture, 155, 378-385. doi: https://doi.org/10.1016/j.compag.2018.10.028

Marqués-Mateu, Á.; Moreno-Ramón, H.; Balasch, S.; Ibáñez-Asensio, S. (2018). Quantifying the uncertainty of soil colour measurements with Munsell charts using a modified attribute agreement analysis. Catena, 171, 44-53. doi: https://doi.org/10.1016/j.catena.2018.06.027

Munsell color company (2009). Munsell Soil Color Charts. X-rite: Grand Rapids. 36 p.

Pegalajar, M.C.; Ruiz, L.G.B.; Sánchez-Marañón, M.; Mansilla, L. (2019). A Munsell colour-based approach for soil classification using Fuzzy Logic and Artificial Neural Networks. Fuzzy Sets and Systems. doi: https://doi.org/10.1016/j.fss.2019.11.002

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

Santos, J.F.C.; Silva, H.R.F.; Pinto, F.A.C.; Assis, I.R. (2016). Use of digital images to estimate soil moisture. Revista Brasileira de Engenharia Agrícola e Ambiental, 20, 1051-1056. doi: http://dx.doi.org/10.1590/1807-1929/agriambi.v20n12p1051-1056

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

Vieira, J. M. (2013). Contribuição de compostos de baixa cristalinidade e ciclos de umedecimento e secagem na gênese do caráter coeso em solos do Ceará.