Abstract
Soil is one of the natural material, which has the different features for the particular characteristics. In digital image processing is the principle to simplify the identification of soil features. Soil consists of both physical and chemical characteristics. These characteristics are used to find the field of soil usage. Thresholding is the conversion of colour image into binary image and that is used for shape based identification. It applicable for feature extract from curvature, valleys, and non-smoothening surfaces and it enhances the feature and get more information. Fractal dimension is one of the soil feature. A new model is proposed to assign various threshold values apply to the same sample and to determine the range and also the best image model (Red-Green-Blue, Hue-Saturation-Value, Hue-Saturation-Luminance and Hue-Saturation-Intensity) of soil samples. The device can also be modelled as most powerful tool for prediction of land usage for various fields such as agriculture and construction.
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References
Adar, S., Shkolnisky, Y., Ben-Dor, E.: Change detection of soils under small-scale laboratory conditions using imaging spectroscopy sensors. Geoderma 216, 19–29 (2014)
Garcia, J., Barbedo, A.: Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2, 660 (2013)
Camargo, A., Smith, J.S.: Image pattern classification for the identification of disease causing agents in plants. Comput. Electron. Agric. 66, 121–125 (2009)
Macedo-Cruz, A., Pajares, G., Villegas-Romero, I., Pajares, G.: Digital image sensor-based assessment of the status of oat (Avena sativa L.) crops after frost damage. Sensors (2009). doi:10.3390/s110606015
Moranduzzo, T., Melgani, F.: Automatic car counting method for unmanned aerial vehicle images. IEEE Trans. Geosci. Remote Sens. 52, 1635 (2014)
Baveye, P.C., Laba, M., Otten, W.: Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray micro tomography data. Geoderma 157, 51–63 (2010)
Berbar, M.A., Laba, M., Otten, W.: Skin colour correction and faces detection techniques based on HSL and R colour components. Int. J. Sign. Imag. Syst. Eng. 7, 104 (2014)
Ding, Z., Zhang, R., Kan, Z.: Quality and safety inspection of food and agricultural products by lab VIEW IMAQ vision. Food Anal. Methods (2014). doi:10.1007/s12161-014-9989-1
Raje, C., and Rangole, J.: Detection of leukemia in microscopic images using image processing. In: International Conference on Communication and Signal Processing (2014)
Oleschko, K., Korvin, G., Munoz, A.: Mapping soil fractal dimension in agricultural fields with GPR. Nonlin. Process. Geophys. 15, 711–725 (2008)
Hocevar, M., Brane, S., Godes, T.: Flowering estimation in apple orchards by image analysis. Precis. Agric. (2014). doi:10.1007/s11119-013-9341-6
Sharma, Y., Meghrajani, Y.K.: Brain tumor extraction from mri image using mathematical morphological reconstruction. 978-1-4799-6986-9/14/$31.00/2014
Ghamisi, P., Couceiro, S.: Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. 0196-2892/$31.00/2013
Minervini, M., Abde, T., Tsaftaris, S.A.: Image-based plant phenol typing with incremental learning and active contours. Ecol. Inform. 23, 35 (2013)
Lloret, J., Bosch, I., Sendra, S., Serrano, A.: A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. Sensors (2014). doi:10.3390/s110606165
Yachun. W., Zhanliang. C., Hongda. W.: Grading method of leaf spot disease based on image processing. In: International Conference on Computer Science and Software Engineering (2008)
Acknowledgements
The author would like to thank the Sir. C.V. RAMAN KRISHNAN International Research Centre for providing financial assistance under the University Research Fellowship. Also we thank the Department of Electronics and Communication Engineering of Kalasalingam University, (Kalasalingam Academy of Research and Education), Tamil Nadu, India for permitting to use the computational facilities available in Centre for Research in Signal Processing and VLSI Design which was setup with the support of the Department of Science and Technology (DST), New Delhi under FIST Program in 2013 (Reference No: SR/FST/ETI-336/2013 dated November 2013).
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Arunpandian, M., Arunprasath, T., Vishnuvarthanan, G., Rajasekaran, M.P. (2018). Thresholding Based Soil Feature Extraction from Digital Image Samples – A Vision Towards Smarter Agrology. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-319-63673-3_55
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DOI: https://doi.org/10.1007/978-3-319-63673-3_55
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