Soil Color Comparison Using Munsell Soil Color Chart and Calibrated Smartphone Camera

Valensi Kautsar, Kuni Faizah, Arief Ika Uktoro

Abstract


Soil color is a crucial property in soil fertility assessment and monitoring. However, the subjective nature of the Munsell Soil Color Chart (MSCC) can lead to uncertainty in the analysis. To address this issue, a study was conducted to develop a soil color classification model from smartphone digital imagery based on color analysis and MSCC. The study involved taking 26 soil samples from various soil types and locations in the Special Region of Yogyakarta, Indonesia. Digital images of the soil were taken through a smartphone camera and compared with observations using MSCC to compare color differences (ΔE) based on Lab values. Soil images obtained from indoor studio conditions and calibration using spydercheckr in indoor and outdoor conditions are compared with MSCC and Chromameter values. The L*a*b color space was found to be superior to RGB for predicting and detecting small differences in color. The study also found that the Munsell soil color chart (MSCC) had a lower color difference than the chromameter in all lighting conditions, indicating that the MSCC or visual assessment can better detect the main soil color or soil matrix, while chromameter readings may have errors due to soil impurities.


Keywords


color difference (∆E); L*a*b; RGB; image processing.

Full Text:

PDF

References


Azetsu, T., & Suetake, N. (2021). Chroma Enhancement in CIELAB Color Space Using a Lookup Table. Designs, 5(2), Article 2. https://doi.org/10.3390/designs5020032

Belosokhov, A., Yarmeeva, M., Kokaeva, L., Chudinova, E., Mislavskiy, S., & Elansky, S. (2022). Trichocladium solani sp. Nov.—A New Pathogen on Potato Tubers Causing Yellow Rot. Journal of Fungi, 8(11), Article 11. https://doi.org/10.3390/jof8111160

Bloch, L. C., Hosen, J. D., Kracht, E. C., LeFebvre, M. J., Lopez, C. J., Woodcock, R., & Keegan, W. F. (2021). Is It Better to Be Objectively Wrong or Subjectively Right? 9(2), 132–144. https://doi.org/10.1017/aap.2020.53

Connolly, C., & Fleiss, T. (1997). A study of efficiency and accuracy in the transformation from RGB to CIELAB color space | IEEE Journals & Magazine | IEEE Xplore. 6(7), 1046–1048. https://doi.org/10.1109/83.597279

Costa, J. J. F., Giasson, É., da Silva, E. B., Coblinski, J. A., & Tiecher, T. (2020). Use of color parameters in the grouping of soil samples produces more accurate predictions of soil texture and soil organic carbon. Computers and Electronics in Agriculture, 177, 105710. https://doi.org/10.1016/j.compag.2020.105710

Ebner, M., Nabavi, E., Shapey, J., Xie, Y., Liebmann, F., Spirig, J. M., Hoch, A., Farshad, M., Saeed, S. R., Bradford, R., Yardley, I., Ourselin, S., Edwards, A. D., Führnstahl, P., & Vercauteren, T. (2021). Intraoperative hyperspectral label-free imaging: From system design to first-in-patient translation. Journal of Physics D: Applied Physics, 54(29), 294003. https://doi.org/10.1088/1361-6463/abfbf6

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(5), 1139–1146. https://doi.org/10.2136/sssaj2017.01.0009

Gómez-Robledo, L., López-Ruiz, N., Melgosa, M., Palma, A. J., Capitán-Vallvey, L. F., & Sánchez-Marañón, M. (2013). Using the mobile phone as Munsell soil-colour sensor: An experiment under controlled illumination conditions. Computers and Electronics in Agriculture, 99, 200–208. https://doi.org/10.1016/j.compag.2013.10.002

Gozukara, G., Zhang, Y., & Hartemink, A. E. (2021). Using vis-NIR and pXRF data to distinguish soil parent materials – An example using 136 pedons from Wisconsin, USA. Geoderma, 396, 115091. https://doi.org/10.1016/j.geoderma.2021.115091

Gunawan, J., Hazriani, R., & Mahardika, R. Y. (2020). Buku Ajar Morfologi dan Klasifikasi Tanah. Fakultas Pertanian Universitas Tanjungpura.

Hall, G. F., Smeck, N. E., & Wilding, L. P. (1983). Pedogenesis and soil taxonomy: Vol. II. The Soil Orders. Elsevier Distributors for the U.S. and Canada, Elsevier Science Pub. Co.

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. https://doi.org/10.1016/j.compag.2016.02.024

Kirillova, N. P., Vodyanitskii, Yu. N., & Sileva, T. M. (2015). Conversion of soil color parameters from the Munsell system to the CIE-L*a*b* system. Eurasian Soil Science, 48(5), 468–475. https://doi.org/10.1134/S1064229315050026

Liu, F., Rossiter, D. G., Zhang, G.-L., & Li, D.-C. (2020). A soil colour map of China. Geoderma, 379, 114556. https://doi.org/10.1016/j.geoderma.2020.114556

Luo, M. R. (2014). CIELAB. In R. Luo (Ed.), Encyclopedia of Color Science and Technology (pp. 1–7). Springer. https://doi.org/10.1007/978-3-642-27851-8_11-1

Mancini, M., Weindorf, D. C., Monteiro, M. E. C., de Faria, Á. J. G., dos Santos Teixeira, A. F., de Lima, W., de Lima, F. R. D., Dijair, T. S. B., Marques, F. D., Ribeiro, D., Silva, S. H. G., Chakraborty, S., & Curi, N. (2020). From sensor data to Munsell color system: Machine learning algorithm applied to tropical soil color classification via NixTM Pro sensor. Geoderma, 375, 114471. https://doi.org/10.1016/j.geoderma.2020.114471

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. https://doi.org/10.1016/j.catena.2018.06.027

Milotta, F. L. M., Furnari, G., Quattrocchi, C., Pasquale, S., Allegra, D., Gueli, A. M., Stanco, F., & Tanasi, D. (2020). Challenges in automatic Munsell color profiling for cultural heritage. Pattern Recognition Letters, 131, 135–141. https://doi.org/10.1016/j.patrec.2019.12.008

Moore, C. A., Brown, A. E., Sias, C. A., Robinson, T. R., & Allik, T. H. (2021). Performance characterization of low light level color imaging sensors. Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXII, 11740, 34–49. https://doi.org/10.1117/12.2585777

Nodi, S. S., Paul, M., Robinson, N., Wang, L., & Rehman, S. U. (2023). Determination of Munsell Soil Colour Using Smartphones. Sensors, 23(6), 3181. https://doi.org/10.3390/s23063181

Pegalajar, M. C., Ruiz, L. G. B., & Criado-Ramón, D. (2023). Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution.

Pegalajar, M. C., Ruiz, L. G. B., Sánchez-Marañón, M., & Mansilla, L. (2020). A Munsell colour-based approach for soil classification using Fuzzy Logic and Artificial Neural Networks. Fuzzy Sets and Systems, 401, 38–54. https://doi.org/10.1016/j.fss.2019.11.002

Pegalajar, M. C., Sánchez-Marañón, M., Baca Ruíz, L. G., Mansilla, L., & Delgado, M. (2018). Artificial Neural Networks and Fuzzy Logic for Specifying the Color of an Image Using Munsell Soil-Color Charts. In J. Medina, M. Ojeda-Aciego, J. L. Verdegay, D. A. Pelta, I. P. Cabrera, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations (pp. 699–709). Springer International Publishing. https://doi.org/10.1007/978-3-319-91473-2_59

Priandana, K., S, A. Z., & Sukarman. (2014). Mobile Munsell Soil Color Chart Berbasis Android Menggunakan Histogram Ruang Citra HVC dengan Klasifikasi KNN. Jurnal Ilmu Komputer Dan Agri-Informatika, 3(2), 93–101. https://doi.org/10.29244/jika.3.2.93-101

Sánchez-Marañón, M., García, P. A., Huertas, R., Hernández-Andrés, J., & Melgosa, M. (2011). Influence of Natural Daylight on Soil Color Description: Assessment Using a Color-Appearance Model. Soil Science Society of America Journal, 75(3), 984–993. https://doi.org/10.2136/sssaj2010.0336

Sánchez-Marañón, M., Huertas, R., & Melgosa, M. (2005). Colour variation in standard soil-colour charts. Soil Research, 43(7), 827–837. https://doi.org/10.1071/SR04169

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), Article 7. https://doi.org/10.1038/nmeth.2089

Sinclair, R., Nodi, S., & Kabir, M. A. (2024). Evaluating mobile applications for estimating soil properties: Quality of current apps, limitations and future directions. Computers and Electronics in Agriculture, 216, 108527. https://doi.org/10.1016/j.compag.2023.108527

Souza, W. S., Oliveira, M. A. S. de, Oliveira, G. M. F. de, Santana, D. P. de, & Araujo, R. E. de. (2018). Self-Referencing Method for Relative Color Intensity Analysis Using Mobile-Phone. Optics and Photonics Journal, 8(7), Article 7. https://doi.org/10.4236/opj.2018.87022

Zhang, X., Liu, H., Zhang, X., Yu, S., Dou, X., Xie, Y., & Wang, N. (2018). Allocate soil individuals to soil classes with topsoil spectral characteristics and decision trees. Geoderma, 320, 12–22. https://doi.org/10.1016/j.geoderma.2018.01.023

Zhang, Y., & Hartemink, A. E. (2019). Digital mapping of a soil profile. 70(1), 27–41. https://doi.org/10.1111/EJSS.12699

Zhu, A.-X., Qi, F., Moore, A., & Burt, J. E. (2010). Prediction of soil properties using fuzzy membership values. Geoderma, 158(3), 199–206. https://doi.org/10.1016/j.geoderma.2010.05.001




DOI: https://doi.org/10.24198/jt.vol18n1.3

Refbacks

  • There are currently no refbacks.


Indexed by:

  

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY-SA 4.0)