Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution Pegalajar Jiménez, María Del Carmen Baca Ruiz, Luis Gonzaga Artificial neural networks Colour matching Munsell soil-colour chart Multiclassification Colour is a property widely used in many fields to extract information in several ways. In soil science, colour provides information regarding the chemical and physical characteristics of soil, such as genesis, composition, and fertility, amongst others. Thus, accurate estimation of soil colour is essential for many disciplines. To achieve this, experts traditionally rely on comparing Munsell colour charts with soil samples, which is a laborious process. In this study, we proposed using artificial neural networks to catalogue soil colour with a two-step classification. Firstly, the hue variable is estimated, and then the remaining two coordinates, value and chroma. Our experiments were conducted using three different, common cameras (one digital camera and two mobile phones). The results of our tests showed a 20% improvement in classification accuracy using the lowest-quality camera and an average accuracy of over 90%. 2023-05-09T09:50:32Z 2023-05-09T09:50:32Z 2023-02-10 info:eu-repo/semantics/article Pegalajar, M.C.; Ruiz, L.G.B.; Criado-Ramón, D. Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution. AgriEngineering 2023, 5, 355–368. [https://doi.org/10.3390/agriengineering5010023] https://hdl.handle.net/10481/81412 10.3390/agriengineering5010023 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI