Development of portable color detector: its application for determination of Munsell Soil Color
DOI:
https://doi.org/10.20961/stjssa.v22i1.91351Keywords:
Color detector, Munsell soil color chart, Portable device, Soil color, TCS3200 sensorAbstract
Soil color is a crucial indicator in soil science and agriculture; it provides information about soil properties and conditions. Typically, surveyors determine soil color by visually comparing the soil samples to the Munsell Soil Color Chart (MSCC). However, the accuracy of this method can be influenced by lighting conditions and the observer's subjectivity, leading to potential inconsistencies. This study introduces a portable color sensor device designed to improve the accuracy and consistency in determining the soil color and its MSCC notation compared to traditional visual methods. The device integrates a TCS3200 color sensor with a microcontroller to automate the color determination process. The device was validated by operating it to determine the color of 12 test paper sheets and four test soil types. The device can determine the color of the tested paper and soil well (100% accuracy); the result is displayed on the Liquid Crystal Display. It consistently achieved 100% accuracy for all measurements with varying ambient light intensity. The device is designed to be portable and easy to use, thus supporting field use for surveyors. Therefore, this device offers significant advantages in soil classification, fertility assessment, and environmental monitoring.Downloads
References
Abd-Elmabod, S. K., Bakr, N., Muñoz-Rojas, M., Pereira, P., Zhang, Z., Cerdà , A., . . . Jones, L. (2019). Assessment of Soil Suitability for Improvement of Soil Factors and Agricultural Management. Sustainability, 11(6), 1588. https://doi.org/10.3390/su11061588
Agmal, S., Prakosa, J. A., & Astuti, C. (2021, 2-2 Oct. 2021). Measurement Uncertainty Analysis of the Embedded System of Microcontroler for An Accurate Timer/Stopwatch. 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE). https://doi.org/10.1109/ICEEIE52663.2021.9616813
Bao, X., Jiang, S., Wang, Y., Yu, M., & Han, J. (2018). A remote computing based point-of-care colorimetric detection system with a smartphone under complex ambient light conditions [10.1039/C7AN01685A]. Analyst, 143(6), 1387-1395. https://doi.org/10.1039/C7AN01685A
Baumann, K., Schöning, I., Schrumpf, M., Ellerbrock, R. H., & Leinweber, P. (2017). Corrigendum to “Rapid assessment of soil organic matter: Soil color analysis and Fourier transform infrared spectroscopy†[Geoderma 278 (2016) 49–57]. Geoderma, 301, 80. https://doi.org/10.1016/j.geoderma.2017.03.018
Djama, Z. A., Kavaklıgil, S. S., & Erşahin, S. (2023). Evaluation of Soil Color and Soil Fertility Relations on Cultivated Semi-Arid Sloping Landscapes [Ekili Yarı-Kurak Eğimli Bir Arazide Toprak Rengi ve Toprak Verimliliği Arasındaki İlişkinin Değerlendirilmesi]. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG), 40(1), 19-25. https://doi.org/10.55507/gopzfd.1213097
Fan, Z., Herrick, J. E., Saltzman, R., Matteis, C., Yudina, A., Nocella, N., . . . 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
Ge, L., Ju, R., Ren, T., & Wu, G. (2015). Interactive RGB-D Image Segmentation Using Hierarchical Graph Cut and Geodesic Distance. In Y.-S. Ho, J. Sang, Y. M. Ro, J. Kim, & F. Wu, Advances in Multimedia Information Processing -- PCM 2015 Cham. https://doi.org/10.1007/978-3-319-24075-6_12
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
Gómez Samus, M., Comerio, M., Montes, M. L., Boff, L., Löffler, J., Mercader, R. C., & Bidegain, J. C. (2021). The origin of gley colors in hydromorphic vertisols: the study case of the coastal plain of the RÃo de la Plata estuary. Environmental Earth Sciences, 80, 1-15. https://doi.org/10.1007/s12665-021-09391-2
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
Harini, B. W., Edy, B. Y., Haryanto, A. S., Martanto, M., Prabowo, P. S., & Prabowo, I. A. (2024). Waste Sorting Machine Automatic of Organic and Inorganic Using Arduino Mega as Microcontroller: Implication for Environmental Sustainability. International Journal of Hydrological and Environmental for Sustainability, 3(2), 74-88. https://journal.foundae.com/index.php/ijhes/article/view/449
Hong, H., Fang, Q., Cheng, L., Wang, C., & Churchman, G. J. (2016). Microorganism-induced weathering of clay minerals in a hydromorphic soil. Geochimica et Cosmochimica Acta, 184, 272-288. https://doi.org/10.1016/j.gca.2016.04.015
Huynh, Q. K., Nguyen, C. N., Tran, N. P. L., Vo, N. H. P., Huynh, T. T., & Nguyen, V. C. (2022). Evaluating the optimal working parameters of the color sensor TCS3200 in the fresh chili destemming system. CTU Journal of Innovation and Sustainable Development, 14(1), 35-42. https://doi.org/10.22144/ctu.jen.2022.004
Ibáñez-Asensio, S., Marqués-Mateu, A., Moreno-Ramón, H., & Balasch, S. (2013). Statistical relationships between soil colour and soil attributes in semiarid areas. Biosystems Engineering, 116(2), 120-129. https://doi.org/10.1016/j.biosystemseng.2013.07.013
Kang, Y.-G., Lee, J.-Y., Kim, J.-H., & Oh, T.-K. (2024). Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique. Agronomy Journal, 116(3), 982-989. https://doi.org/10.1002/agj2.21525
Kim, Y., & Seo, S. C. (2021). Efficient Implementation of AES and CTR_DRBG on 8-Bit AVR-Based Sensor Nodes. IEEE Access, 9, 30496-30510. https://doi.org/10.1109/ACCESS.2021.3059623
Kirillova, N. P., Grauer-Gray, J., Hartemink, A. E., Sileova, T. M., Artemyeva, Z. S., & Burova, E. K. (2018). New perspectives to use Munsell color charts with electronic devices. Computers and Electronics in Agriculture, 155, 378-385. https://doi.org/10.1016/j.compag.2018.10.028
Kirillova, N. P., Vodyanitskii, Y. 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
Kirillova, N. P., Zhang, Y., Hartemink, A. E., Zhulidova, D. A., Artemyeva, Z. S., & Khomyakov, D. M. (2021). Calibration methods for measuring the color of moist soils with digital cameras. CATENA, 202, 105274. https://doi.org/10.1016/j.catena.2021.105274
Li, J., Hanselaer, P., & Smet, K. A. G. (2022). Impact of Color-Matching Primaries on Observer Matching: Part I – Accuracy. LEUKOS, 18(2), 104-126. https://doi.org/10.1080/15502724.2020.1864395
Lita, I., Visan, D. A., Ionescu, L. M., & Mazare, A. G. (2019, 27-29 June 2019). Color-Based Sorting System for Agriculture Applications. 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). https://doi.org/10.1109/ECAI46879.2019.9041923
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
Liu, G., & Duan, J. (2020). RGB-D image segmentation using superpixel and multi-feature fusion graph theory. Signal, Image and Video Processing, 14(6), 1171-1179. https://doi.org/10.1007/s11760-020-01647-x
Long, X., Ji, J., Barrón, V., & Torrent, J. (2016). Climatic thresholds for pedogenic iron oxides under aerobic conditions: Processes and their significance in paleoclimate reconstruction. Quaternary Science Reviews, 150, 264-277. https://doi.org/10.1016/j.quascirev.2016.08.031
Luján Soto, R., Cuéllar Padilla, M., & de Vente, J. (2020). Participatory selection of soil quality indicators for monitoring the impacts of regenerative agriculture on ecosystem services. Ecosystem Services, 45, 101157. https://doi.org/10.1016/j.ecoser.2020.101157
Mancini, M., Weindorf, D. C., Monteiro, M. E. C., de Faria, Ã. J. G., dos Santos Teixeira, A. F., de Lima, W., . . . Curi, N. (2020). From sensor data to Munsell color system: Machine learning algorithm applied to tropical soil color classification via Nixâ„¢ Pro sensor. Geoderma, 375, 114471. https://doi.org/10.1016/j.geoderma.2020.114471
McAlinden, C., Khadka, J., & Pesudovs, K. (2015). Precision (repeatability and reproducibility) studies and sample-size calculation. J Cataract Refract Surg, 41(12), 2598-2604. https://doi.org/10.1016/j.jcrs.2015.06.029
Mohd Khairudin, A. R., Abdul Karim, M. H., Samah, A. A., Irwansyah, D., Yakob, M. Y., & Zian, N. M. (2021, 10-11 Dec. 2021). Development of Colour Sorting Robotic Arm Using TCS3200 Sensor. 2021 IEEE 9th Conference on Systems, Process and Control (ICSPC 2021). https://doi.org/10.1109/ICSPC53359.2021.9689114
Moritsuka, N., Matsuoka, K., Katsura, K., Sano, S., & Yanai, J. (2014). Soil color analysis for statistically estimating total carbon, total nitrogen and active iron contents in Japanese agricultural soils. Soil Science and Plant Nutrition, 60(4), 475-485. https://doi.org/10.1080/00380768.2014.906295
Moshago, S., Regassa, A., & Yitbarek, T. (2022). Characterization and Classification of Soils and Land Suitability Evaluation for the Production of Major Crops at Anzecha Watershed, Gurage Zone, Ethiopia. Applied and Environmental Soil Science, 2022(1), 9733102. https://doi.org/10.1155/2022/9733102
Munsell Color Company. (2000). Munsell Soil Color Charts, Munsell Color. Macbeth Division of Kollmorgen Instruments Corporation, New Windsor, NY, USA. https://munsell.com/color-products/color-communications-products/environmental-color-communication/munsell-soil-color-charts/
Nixon, M., Outlaw, F., & Leung, T. S. (2020). Accurate device-independent colorimetric measurements using smartphones. PLOS ONE, 15(3), e0230561. https://doi.org/10.1371/journal.pone.0230561
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
Nunes, M. R., Veum, K. S., Parker, P. A., Holan, S. H., Karlen, D. L., Amsili, J. P., . . . Moorman, T. B. (2021). The soil health assessment protocol and evaluation applied to soil organic carbon. Soil Science Society of America Journal, 85(4), 1196-1213. https://doi.org/10.1002/saj2.20244
Ochoa-López, G., Revilla-León, M., & Gómez-Polo, M. (2024). Impact of color temperature and illuminance of ambient light conditions on the accuracy of complete-arch digital implant scans. Clinical Oral Implants Research, 35(8), 898-905. https://doi.org/10.1111/clr.14220
Pegalajar, M. C., Ruiz, L. G. B., & Criado-Ramón, D. (2023). Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution. AgriEngineering, 5(1), 355-368. https://doi.org/10.3390/agriengineering5010023
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
Sánchez-Marañón, M., Romero-Freire, A., & MartÃn-Peinado, F. J. (2015). Soil-color changes by sulfuricization induced from a pyritic surface sediment. CATENA, 135, 173-183. https://doi.org/10.1016/j.catena.2015.07.023
Saparullah, R., Pebralia, J., & Maulana, L. Z. (2024). Internet of Things (IoT) and Arduino IDE as a Smart Water Quality Control for Monitoring in Catfish Ponds. International Journal of Hydrological and Environmental for Sustainability, 3(1), 48-56. https://doi.org/10.58524/ijhes.v3i1.415
Schmidt, S. A., & Ahn, C. (2019). A Comparative Review of Methods of Using Soil Colors and their Patterns for Wetland Ecology and Management. Communications in Soil Science and Plant Analysis, 50(11), 1293-1309. https://doi.org/10.1080/00103624.2019.1604737
Schröder, S., Eppig, T., & Langenbucher, A. (2016). A Concept for the analysis of repeatability and precision of corneal shape measurements. Zeitschrift für Medizinische Physik, 26(2), 150-158. https://doi.org/10.1016/j.zemedi.2016.01.002
Silva, L. S., Marques Júnior, J., Barrón, V., Gomes, R. P., Teixeira, D. D. B., Siqueira, D. S., & Vasconcelos, V. (2020). Spatial variability of iron oxides in soils from Brazilian sandstone and basalt. CATENA, 185, 104258. https://doi.org/10.1016/j.catena.2019.104258
Simon, T., Zhang, Y., Hartemink, A. E., Huang, J., Walter, C., & Yost, J. L. (2020). Predicting the color of sandy soils from Wisconsin, USA. Geoderma, 361, 114039. https://doi.org/10.1016/j.geoderma.2019.114039
SolÃs, M., Muñoz-Alvarado, E., & Pegalajar, M. C. (2022). The transformation of RGB images to Munsell Soil-Color charts. Uniciencia, 36(1), 559-568. https://doi.org/10.15359/ru.36-1.36
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, 141-148. https://doi.org/10.1016/j.compag.2015.11.014
Taneja, P., Vasava, H. K., Daggupati, P., & Biswas, A. (2021). Multi-algorithm comparison to predict soil organic matter and soil moisture content from cell phone images. Geoderma, 385, 114863. https://doi.org/10.1016/j.geoderma.2020.114863
Vasu, D., Srivastava, R., Patil, N. G., Tiwary, P., Chandran, P., & Kumar Singh, S. (2018). A comparative assessment of land suitability evaluation methods for agricultural land use planning at village level. Land Use Policy, 79, 146-163. https://doi.org/10.1016/j.landusepol.2018.08.007
Vaysse, K., Heuvelink, G. B. M., & Lagacherie, P. (2017). Spatial aggregation of soil property predictions in support of local land management. Soil Use and Management, 33(2), 299-310. https://doi.org/10.1111/sum.12350
Wills, S. A., Burras, C. L., & Sandor, J. A. (2007). Prediction of Soil Organic Carbon Content Using Field and Laboratory Measurements of Soil Color. Soil Science Society of America Journal, 71(2), 380-388. https://doi.org/10.2136/sssaj2005.0384
Yang, J., Shen, F., Wang, T., Luo, M., Li, N., & Que, S. (2021). Effect of smart phone cameras on color-based prediction of soil organic matter content. Geoderma, 402, 115365. https://doi.org/10.1016/j.geoderma.2021.115365
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial License that allows others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material) the work non-commercially with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).


.png)











