Estimation of Soil Organic Carbon using Artificial Neural Network and Multiple Linear Regression Models based on Color Image Processing

ماشین های کشاورزی

Article 11, Volume 8, Issue 1 - Serial Number 15, 2018, Pages 137-148   

Document Type: Research Article

DOI: 10.22067/jam.v8i1.59228

Authors

P. Ataieyan¹; P. Ahmadi Moghaddam^{* 1}; E. Sepehr²

¹Department of Biosystem Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran

²Department of Soil Science, Faculty of Agriculture, Urmia University, Urmia, Iran

Abstract

Introduction
The color of soil depends on its composition and this feature is easily available and rather stable. Fast and accurate determination of soil organic matter distribution in the agricultural fields is required, especially in precision farming. General laboratory methods for determining the soil organic carbon are expensive, time-consuming with many repetitions, and high consumption of chemicals. Soil scientists use the Munsell soil color diagrams to define the soil color. Due to the nature of Munsell color diagrams; this system is less suitable for recognizing exact color of the soil because of weak relationship and limited number of chips. Fast methods like image processing, colorimetric and spectroscopy provide a description of most physical characteristics of the soil color. Some of the advantages of using digital cameras was used in this study, are simple sampling of screened soil, being free from chemicals and toxic materials and being fast, inexpensive and precise.
Materials and Methods
In this research, 80 A-horizon (0-10 cm) soil samples were collected from various agricultural soils in west Azerbaijan, in the North West of Iran. Soil texture of these fields was loam clay and clay. The amount of organic carbon in samples was determined. The camera was installed at the distance of 0.5 m from the Petri dish on the lighting compartment. Captured images with the digital camera were saved in RGB color space. Processing operations were done by MATLAB 2012 software. The features extracted from the color images are used to model the soil organic carbon including the color features in different spaces. Four-color spaces including RGB, HSI, LAB and LUV were studied to find the relation between the color and the soil organic carbon.
Results and Discussion
The correlation of R component in the RGB model shows a strong single-parameter relation with the organic carbon as R²=0.83. This good relationship can be due to the compound information of the red color component on both brightness and chromaticity dimension. The results also show that the organic carbon has a relatively strong correlation with the light parameters, intensity and lightness in the HSI, Lab and LUV color spaces respectively. It also has a weak correlation with other parameters, since they cannot have a proper linear correlation with organic carbon due to their structural nature. Results show that the highest correlation is obtained when the R and G components participate in modeling and the component B is omitted. One explanation of this high correlation could be the high sensitivity of component B to the intensity and the angle of light. Even a small change in light changes this component. Thus, in order to reduce the effect of this component, it is better to omit it from the models and make models independent of it. In next section, 51 data were used to train neural network, 14 data were used to test the network and 12 data for network validating. The amount of soil organic carbon was output of the neural networks that was estimated after training using the color component values of each segment. To assess the accuracy of the network, estimated values and actual values of each sample were plotted in a graph. The minimum MSE values were 7.28e-6 with 16 neurons, 3.77e-6 with 14 neurons, 4.8e-3 with 10 neurons and 3.77e-6 with 12 neurons for RGB, HSI, Lab and LUV color spaces respectively.
Conclusion
The availability of digital cameras, possibility to use it in different situations, being inexpensive and providing many samples are the advantages of this method to estimate the soil organic carbon amount. Therefore, digital photography can be used as an analytical method to evaluate the soil fertility. It also requires a low cost of sample testing, and can provide a good possibility of time and place classification for studying a vast area. However to develop more reliable models, more effort is needed, such as collecting more soil samples of different areas that include a wide range of soil features.

Keywords

Digital camera; Neural Networks; Organic carbon; Precision agriculture

References

1. Aglave, V. A., S. B. Patil, and N. B. Sambre. 2012. Imaging technique to measure leaf area, Disease Severity and Chlorophyll Content: A Survey Paper. Journal of Computing Technologies 1 (3).

2. Barrett, L. R. 2002. Spectrophotometric color measurement in situ in well drained sandy soils. Geoderma 108 (1): 49-77.

3. Blackmer, A., and S. White. 1998. Blue arrow e-Alerts. Australian Journal of Agricultural Research 49 (3): 555-564.

4. Cheng, H. D., X. H. Jiang, Y. Sun, and J. Wang. 2001. Color image segmentation: advances and prospects. Pattern Recognition 34: 2259-2281.

5. Christy, C. D. 2008. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Computers and Electronics in Agriculture 61 (1): 10-19.

6. DE L'ECLAIRAGE–CIE, C. I. 1978. Recommendations on uniform color spaces, color difference equations and psychometric color terms. CIE Publication (15): 9-12.

7. Fortner, B., and T. E. Meyer. 1997. Number by colors: a guide to using color to understand technical data. TELOS, Electronic Library of Science.

8. Gonzalez, R. C., and R. E. Woods. 2002. Digital image processing. Prentice hall Upper Saddle River, NJ.

9. Huang, X., S. Senthilkumar, A. Kravchenko, K. Thelen, and J. Qi. 2007. Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information. Geoderma 141 (1): 34-42.

10. Kamali, M., S. J. Razavi, M. Sadeghi, and S. M. Shafaei. 2014. Modeling moisture absorption kinetics of barley grain using viscoelastic model and neural networks. Journal of Agricultural Machinery 5 (2): 270-280. (In Farsi).

11. Konen, M., C. Burras, and J. Sandor. 2003. Organic carbon, texture, and quantitative color measurement relationships for cultivated soils in north central Iowa. Soil Science Society of America Journal 67 (6): 1823-1830.

12. Levin, N., E. Ben‐Dor, and A. Singer. 2005. A digital camera as a tool to measure colour indices and related properties of sandy soils in semi‐arid environments. International Journal of Remote Sensing 26 (24): 5475-5492.

13. McCauley, J., B. Engel, C. Scudder, M. Morgan, and P. Elliott. 1993. Assessing the spatial variability of organic matter, American Society of Agricultural Engineers. Meeting (USA).

14. Melville, M., and G. Atkinson. 1985. Soil colour: its measurement and its designation in models of uniform colour space. Journal of Soil Science 36 (4): 495-512.

15. Payman, S. H., A. Bakhshipour Ziaaratgahi, and A. Jafari. 2014 Exploring the possibility of using digital image processing technique to detect diseases of rice leaf. 2014. Journal of Agricultural Machinery 6 (1): 69-79. (In Farsi).

16. Rossel, R., C. Walter, Y. Fouad, J. Stafford, and A. Werner. 2003. Assessment of two reflectance techniques for the quantification of the within-field spatial variability of soil organic carbon, Precision agriculture: Papers from the 4th European Conference on Precision Agriculture, Berlin, Germany, 15-19 June 2003. Wageningen Academic Publishers, pp. 697-703.

17. Schulze, D. G., J. L. Nagel, G. E. Van Scoyoc, T. L. Henderson, M. F. Baumgardner, and D. Stott. 1993. Significance of organic matter in determining soil colors. Soil color (soilcolor), 71-90.

18. Shonk, J., L. Gaultney, D. Schulze, and G. Van Scoyoc. 1991. Spectroscopic sensing of soil organic matter content. Transactions of the ASAE (USA).

19. Smith, A. R. 1978. Color gamut transform pairs, ACM Siggraph Computer Graphics. ACM, pp. 12-19.

20. Sudduth, K., and J. Hummel. 1993. Soil organic matter, CEC, and moisture sensing with a portable NIR spectrophotometer. Transactions of the ASAE (USA).

21. Viscarra, R., Y. Fouad and C. Walter. 2008. Using a digital camera to measure soil organic carbon and iron contents. Biosystems Engineering 100 (2): 149-159.

22. Viscarra, R., B. Minasny, P. Roudier, and A. McBratney. 2006. Colour space models for soil science. Geoderma 133 (3): 320-337.

23. Walkley, A., and I. A. Black. 1934. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science 37 (1): 29-38.