A smartphone-based soil color sensor: For soil type classification

First study of soil classification using both spectroscopy and smartphone-based sensor.Develop a soil classification sensor that completely based on smartphone and without any passive components.Comparison of spectroscopy and smartphone-based sensor for soil types classification. Soil type is a key indicator in field survey, but the current soil classification method largely depends on personal experiences of operators. Because different soils have different soil colors, soil can be classified by measuring soil colors. In this paper, we compared and analyzed the roles of the visible spectrum and machine vision adopted in soil classification and proposed a new smartphone-based, low-cost, and miniaturized soil color classification sensor. The CMOS device of the mobile phone was directly used as the sensor and the flashgun of the phone was used as the light source. The peripheral components included external lens, shading devices, and color calibration card, which were assembled on the phone directly. The colors of soil and proofread cards were acquired by the smartphone and converted into RGB signals. With RGB signals, after simple processing, rapid soil classification could be achieved.

[1]  Mohammed Feham,et al.  M-Health: Skin Disease Analysis System Using Smartphone's Camera , 2013, ANT/SEIT.

[2]  Philippe Lacomme,et al.  A smartphone-driven methodology for estimating physical activities and energy expenditure in free living conditions , 2014, J. Biomed. Informatics.

[3]  Sangho Lee,et al.  Smart Compass-Clinometer: A smartphone application for easy and rapid geological site investigation , 2013, Comput. Geosci..

[4]  José Torrent,et al.  Laboratory Measurement of Soil Color: Theory and Practice , 2015 .

[5]  Joonas Paalasmaa,et al.  Long-term sleep measurement with a smartphone-connected flexible bed sensor strip , 2013 .

[6]  James A. J. Heathers,et al.  Smartphone-enabled pulse rate variability: an alternative methodology for the collection of heart rate variability in psychophysiological research. , 2013, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[7]  Angus I. Kirkland,et al.  The effects of electron and photon scattering on signal and noise transfer properties of scintillators in CCD cameras used for electron detection , 1998 .

[8]  Alberto J. Palma,et al.  Using the mobile phone as Munsell soil-colour sensor: An experiment under controlled illumination conditions , 2013 .

[9]  W. Hively,et al.  Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey , 2010 .

[10]  J. De Baerdemaeker,et al.  Potential of visible and near-infrared spectroscopy to derive colour groups utilising the Munsell soil colour charts , 2007 .

[11]  Arnaldo J. Abrantes,et al.  Classification of Physical Activities Using a Smartphone: Evaluation Study Using Multiple Users , 2014 .

[12]  Charlie Chen,et al.  Digitally mapping the information content of visible–near infrared spectra of surficial Australian soils , 2011 .

[13]  Salih Aydemir,et al.  Quantification of soil features using digital image processing (DIP) techniques , 2004 .

[14]  José Alexandre Melo Demattê,et al.  Visible–NIR reflectance: a new approach on soil evaluation , 2004 .

[15]  G. Fystro The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods , 2002, Plant and Soil.

[16]  L. Javier García-Villalba,et al.  Smartphone image clustering , 2015, Expert Syst. Appl..

[17]  R. V. Rossel,et al.  Using a digital camera to measure soil organic carbon and iron contents , 2008 .

[18]  Héctor Moreno-Ramón,et al.  Statistical relationships between soil colour and soil attributes in semiarid areas , 2013 .

[19]  Won Suk Lee,et al.  Comparison of Ultraviolet, Visible, and Near Infrared Sensing for Soil Phosphorus , 2007 .

[20]  Subash C B Gopinath,et al.  Bacterial detection: from microscope to smartphone. , 2014, Biosensors & bioelectronics.

[21]  R. V. Rossel,et al.  Colour space models for soil science , 2006 .

[22]  Giuseppe Guido,et al.  Estimation of Safety Performance Measures from Smartphone Sensors , 2012 .

[23]  Egan H. Doeven,et al.  Use of a mobile phone for potentiostatic control with low cost paper-based microfluidic sensors. , 2013, Analytica chimica acta.

[24]  Xusheng Zhang,et al.  Signal-to-noise ratio evaluation of a CCD camera , 2009 .

[25]  Alfred E. Hartemink,et al.  Soil genesis and classification , 2013 .