RELATIONSHIP BETWEEN SOIL MOISTURE CONTENT IN PADDY FIELD AND ITS IMAGE TEXTURE

Authors

  • Siti Khairunniza Bejo Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Nor Hafizah Sumgap Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Siti Nurul Afiah Mohd Johari Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.7275

Keywords:

Soil moisture content, texture, image processing

Abstract

The aim of this study is to identify the relationship between soil moisture content and its image texture. Soil image was captured and converted into CIELUV color space. These images were later used to develop two dimensional gray level co-occurrence matrix. Eight texture features extracted from gray level co-occurrence matrix namely mean, variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation was used for the analysis. The results has shown that the image texture properties can be used to relate with soil moisture content, where variance, homogeneity, dissimilarity, entropy, contrast, second moment and correlation gave significant responds to the moisture content. The highest value of correlation was gathered from entropy with r = -0.522.

References

M. Z Mohamed Azwan., M. Sa’ari and P. Zuzana. 2010. Determination of Water Requirement in Paddy Field at Seberang Perak Rice Cultivation Area. Journal-The Institution of Engineers, Malaysia. 71(4): December 2010.

Blaine, H. and Steve, O. 1998. Measuring Soil Moisture. Davis, USA: University of California

Haralick, R. M., Shanmugam, K., Dinstein, I. 1973. Textural

Features For Image Classification. IEEE Transaction on Systems, Man and Cybernetics. SMC-3(6): 610-621.

Han, Y. J., Hayes, J. C. 1990. Soil Covers Determination By Image Analysis Of Textural Information. Transactions of the ASAE. 33(2): 681-686.

Burks, T. F., Shearer, S. A., Payne, F. A. 1998. Evaluation Of Statistical Discriminant Analysis Techniques For Classification Of Weed Species Using Machine Vision. ASAE Paper No. 98 3037. St. Joseph, MI, USA.

Levin, N., Ben-Dor, E., Singer, A. 2005. A Digital Camera As Tool To Measure Color Indices And Related Properties Of Sandy Soils In Semi-Arid Environments. International Journal of Remote Sensing. 26(24): 5475-5492.

Meyer, G. E., T. Mehta, M. F. Kocher, D. A. Mortensen, and A. Samal. 1998. Textural Imaging And Discriminant Analysis For Distinguishing Weeds For Spot Spraying. Transactions of the ASAE. 41(4): 1189-1197.

Bodun, P. O., Shibusawa, S., Sasao, A., Sakai, K., & Nonaka, H. 2000. Dredged Sludge Moisture Prediction By Textural Analysis Of The Surface Image. Journal of terramechanics. 37(1): 3-20.

Roy, S. K., Shibusawa, S., Kaho, T., Morimoto, E., Sasso, A., Okayama, T., Kondo, N. 2006. Textural Analysis of Soil Images to Quantify and Characterize the Spatial Variation of Soil Properties using a Real-time Soil Sensor. Precision Agric (2006). 7: 419-436

Sumgap, N. H. and Khairunniza-Bejo, S. 2011. Colour Spaces for Paddy Soil Moisture Content Determination. J. Trop. Agric. and Fd. Sc. 39(1)(2011): 103-115.

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Published

2016-01-27

How to Cite

RELATIONSHIP BETWEEN SOIL MOISTURE CONTENT IN PADDY FIELD AND ITS IMAGE TEXTURE. (2016). Jurnal Teknologi (Sciences & Engineering), 78(1-2). https://doi.org/10.11113/jt.v78.7275