Abstract

The traditional soil analysis technique when applied is time consuming, labour intensive and expensive. The research made an attempt to develop an intelligent system that is capable of classifying soil in a particular location if the hyperspectral data of such location is available. The system was developed using unsupervised learning. Wavelet transform was used to denoise the spectral signal at preprocessing stage. Fuzzy c- means was used for clustering in other to identify the cluster centre. KSOM is applied for the purpose of classifying soil into various classes. The system was implemented using R programming language.

References

• Agris Nikitenko (2006). Hybrid Intelligent System Development and Implementation. Summary of Doctoral Thesis. RIGA TECHNICAL UNIVERSITY, Riga.
• Amit Konar (1999). Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain. Department of Electronics and Tele-communication Engineering, Jadavpur University, Calcutta, India. ISBN 0-8493-1385-6 (alk. paper), QA76. 9. S63 K59,pg 28.
• Baojuan Zheng (2008). Using Satellite Hyperspectral Imagery to Map Soil Organic Matter, Total Nitrogen and Total Phosphorus. Thesis Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Master of Science in the Department of Earth Sciences, Indiana University.
• Cécillon, L. ; Barthès, B. G. ; Gomez, C. ; Ertlen, D. ; Genot, V. ; Hedde, M. ; Stevens, A. ; Brun, J. J. Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS). Eur. J. Soil Sci. 2009, 60, 770–784.
• Chang, C. W. , D. A. Laird, M. J. Mausbach, and C. R. Hurburgh (2001). Near-Infrared Reflectance Spectroscopy—Principal Components Regression Analyses of Soil Properties. Soil Sci. Soc. Am. J. , 6: 480-490.
• Chen, F. , D. E Kissel, L. T. West, and W. Adkins (2000). Field-Scale Mapping of Surface Soil Organic Carbon Using Remotely Sensed Imagery. Soil Sci. Soc. Am. J. , 64: 746-753.
• Dalal, R. C. and R. J. Henry (1986). Simultaneous Determination of Moisture, Organic Carbon, and Total Nitrogen by Near Infrared Reflectance Spectrophotometry. Soil Sci. Soc. Am. J. , 50: 120-123.
• Dematte, J. A. M. , M. V. Galdos, R. V. Guimaraes, A. M. Genu, M. R. Nanni, and J. Zullo (2007). Quantification of tropical soil attributes from ETM+/LANDSAT-7 data. Int. J. Remote Sens. , 28(17): 3813-3829.
• ESBN (European Soil Bureau Network), 2005. Soil Atlas of Europe. European Commission, Office for Official Publications of the European Communities, L-2995 Luxemburg.
• He, Y. , M. Huang, A. Garcia, A. Hernandez, and H. Song (2007). Prediction of soil macronutrients content using near-infrared spectroscopy. Comput. Electron. Agr. , 58(2): 144-153.
• Machine Learning, Part I: Supervised and Unsupervised Learning. www. aihorizon. com/essays/generalai
• Matlin, W. Margaret, Cognition, Hault Sounders, printed and circulated by Prism books, India, 1996.
• Muller A. J. and S. Nilsson (2009). Harmonized World Soil Database (version 1. 1), Foreword to FAO/IIASA/ISRIC/ISS-CAS/JRC. FAO, Rome, Italy and IIASA, Laxenburg, Austria.
• Nikola Kasabov (2007). Brain, Gene and Quantum inspired Computational Intelligence: Challenges and Opportunities. Challenges for Computational Intelligence. DOI 10. 1007/978-3-540-71984-7_9, Vol 63, pp193-219
• Ray S. S. , J. P. Singh, G. Das, S. Panigrahy (2004). Use of High Resolution Remote Sensing Data for Generating Site-specific Soil Mangement Plan. XX ISPRS Congress, Commission 7. Istanbul, Turkey The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: 127-131.
• Samet, H. , "Neighbor finding techniques for images represented by quadtrees," Computer, Graphics and Image Processing, vol. 18, pp. 37-57, 1982.
• Sergio Freire, Ines Fonseca, Ricardo Brasil, Jorge Rocha, and Jose A. Tenedorio (2013). Using Artificial Neural Networks for Digital Soil Mapping a comparison of MLP and SOM approaches. AGILE 2013 – Leuven, May 14-17.
• Stephan Gmur, Daniel Vogt, Darlene Zabowski and L. Monika Moskal(2012). Hyperspectral Analysis of Soil Nitrogen, Carbon, Carbonate, and Organic Matter Using Regression Trees. Sensors. ISSN 1424-8220, doi:10. 3390/s120810639, pg 10639-10658. www. mdpi. com/journal/sensors
• Udelhoven, T. , C. Emmerling, and T. Jarmer (2003). Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study. Plant Soil, 251(2): 319-329.
• Vandal Smith (2010). Application of Neural Network in Whether Prediction. "The Pacesetter ". Vol. 10, No 3, 25-35.
• Vogt, K. A. ; Patel-Weynand, T. ; Shelton, M. ; Vogt, D. J. ; Gordon, J. C. ; Mukumoto, C. ; Suntana, A. S. ; Roads, P. A. Sustainability Unpacked: Food, Energy and Water for Resilient Environments and Societies; Earthscan: London, UK, 2010.
• Zhengyong Zhaoa, Thien Lien Chowb, Herb W. Reesb, Qi Yanga, Zisheng Xingb and Fan-Rui Menga (2009). Predict Soil Texture Distributions using an Artificial Neural Network Model. Computers and Electronics in Agriculture. Pg 36–48. www. elsevier. com/locate/compag.
• (www. aihorizon. com/essays/generalai).