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Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper
Concentration
F. Antonucci1, P. Menesatti1*, E. Canali1, S. Giorgi1, A. Maienza2, S.R. Stazi2 1 CRA-ING – Agricultural Research Council, Agricultural Engineering Research Unit, Lab. for Advanced Engineering Applications in Agriculture (AgriTechLab) – Monterotondo (Roma) – Italy 2 Department of Agrobiology and Agrochemistry, University of Tuscia – Viterbo, Italy ABSTRACT
Soil characterization in agriculture represents an important tool to plan its productivity and the quality of the resulted products. It is conventionally performed adopting physical-chemical analyses that classify soil samples delivered to specialized laboratories. The objective of this research is to develop a hyperspectral imaging system to estimate the concentration of copper in topsoil, as an alternative to the standard chemical analyses. Hyperspectral Imaging is a technique of high technological and methodological complexity but with elevated informative content. Sensors collect hyperspectral information as sets of images. The images were acquired within two range of the electromagnetic spectrum: visible-near infrared (VIS-NIR) and near infrared (NIR). These were consequently combined to yield a three dimensional hyperspectral cube for processing and analysis. To carry out and compare the chemical analyses soil samples were primarily air-dried, removing and discarding after sifting the fraction > 2 mm. Metal soil samples were prepared by adding 20 ml of copper sulphate solution (CuSO4•5H2O) to the test soil to concentrations ranging from 1 to 1000 mg of copper per kg of soil step 50 mg. The twenty amended soil samples obtained were oven-dried at 65°C for 48 h to produce samples with the same moisture. Samples were placed into a Duraplan® borosilicate optical-glass Petri dish, 3 for each concentration. Petri dishes were randomly scanned using VIS-NIR and NIR spectrophotometers and spectral data were acquired. Partial Least Squares regressions (PLS) were performed with the software Matlab 7.5 on all the collected spectral arrays, in order to test the ability of the proposed model to quantify the different concentrations of copper content. PLS is a soft-modeling method to built predictive models when the factors are many and highly collinear. The procedure emphasizes the prediction of the responses not necessarily trying to understand the relationship between variables. Results indicate that the correlation between predicted values and the observed chemical values is highly significant with a coefficient of determination (R²) in the test of 0.95 for the VIS-NIR range and of 0.82 for the NIR. This method investigates whether the system can help in explaining heavy metal interactions with other soil properties. This could lead to significant benefits for soil remediation, survey and precision farming, representing a rapid and non-destructive alternative to classical soil analysis for the detection and characterization of copper topsoil concentration. Keywords: Soil characterization, topsoil copper content, hyperspectral images, PLS, chemical
analysis, precision farming.
F. Antonucci, P. Menesatti, E. Canali, S. Giorgi, A. Maienza, S.R. Stazi. “Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper Concentration”. CIGR Workshop on Image Analysis in Agriculture, 26-27. August 2010, Budapest. 1. INTRODUCTION
Soil characterization in agriculture represents an important tool to plan its productivity and the quality of the resulted products. It is conventionally performed adopting physical-chemical analyses that classify soil samples delivered to specialized laboratories. Practical methods that can rapidly estimate soil properties are needed to improve quantitative assessments of land management problems (Shepherd and Walsh, 2002) and to make precision soil management feasible (Viscarra Rossel and McBratney, 1998). Recent developments in imaging spectrophotometry that digitally captures and processes reflectance spectra to quantify soil properties are now desirable developments from spectroscopic techniques (e.g. mass spectroscopy (MS), nuclear magnetic resonance (NMR), visible (VIS), near infrared (NIR) and mid infrared (MIR) spectroscopy) as they offer alternatives to improve or replace conventional laboratory methods of soil analysis (Janik et al., 1998). A great number of these techniques are non-destructive, preserve the integrity of the soil system and provide larger amounts of inexpensive spatial data (Viscarra Rossel et al., 2006). The most recent advances in quantifying soil properties have focused on using narrow waveband visible-near infrared (VIS-NIR) sensing (Viscarra Rossel et al., 2009), while the State of the Art in optical sensing for this type of problem has started to encompass hyperspectral sensing (Ben-Dor et al., 1999). Hyperspectral imaging is a complex technology that provides elevated information content while being rapid, non-destructive and cost-effective. Hyperspectral sensors collect information as sets of “images”. Each image represents a range of the electromagnetic spectrum and is referred to as spectral band. These images are then combined to yield a three dimensional hyperspectral cube for processing and analysis. The technique integrates conventional imaging and spectroscopy to obtain both spatial and spectral information from an object within a large field of view (FOV). Space and airborne remote sensing using hyperspectral sensors to measure soil properties is well established technology (Lagacherie et al., 2008; Gomez et al 2008) but proximal imaging applications are still underdeveloped. Among the hyperspectral techniques thus far examined in the laboratory, VIS-NIR and NIR have been shown to have potential as rapid, non-destructive approaches for analysis of several soil properties, including water, carbon and macronutrient content. The objective of this research is to develop a hyperspectral imaging system to estimate the concentration of copper in topsoil, as an alternative to the standard chemical analyses. 2. MATERIALS AND METHODS
2.1 Soil preparation
To carry out and compare the chemical analyses soil samples were primarily air-dried, removing
and discarding after sifting the fraction > 2 mm. After removal of litter layer soil cores were
taken inside each plot at 20 cm depth and then pooled together. The homogenised soil was then
divided into 20 accurately weighed samples of approximately similar weight. The samples were
then contaminated with copper by adding 20 ml of copper sulphate solution (CuSO4•5H2O) at
concentrations ranging from 0 (control) to 1000 mg of copper per kg of soil.
2.2 Spectrophotometer analysis
F. Antonucci, P. Menesatti, E. Canali, S. Giorgi, A. Maienza, S.R. Stazi. “Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper Concentration”. CIGR Workshop on Image Analysis in Agriculture, 26-27. August 2010, Budapest. Each of the 20 copper contaminated samples was poured into 3 borosilicate optical-glass Petri
dishes (Duraplan®). Each 60 Petri dishes was placed on a black background and acquired for the
hyperspectral imaging. For each hyperspectral image two ROIs (Region Of Interest) to measure
the mean VIS-NIR and NIR spectral reflectance were selected. The imaging spectrometers (VIS-
NIR and NIR) were used to acquire images ranging from 400 to 970 nm and from 1000 to 1700
nm respectively. The two spectrographs are based on a patented prism-grating-prism (PGP)
construction (a holographic transmission grating). The incoming line image (frame) was
projected and dispersed onto the 2D CCD. Each frame contained the line pixels in one dimension
(spatial axis) and the spectral pixels in the other dimension (spectral axis), providing full spectral
information for each line pixel. The reconstruction of the entire hyperspectral image of the
sample was performed by scanning the sample line-by-line as the transportation plate moved it
through the field of view. The system was operated in a dark laboratory to minimize interference
from ambient light. All spectral values were expressed in terms of relative reflectance.
2.3 Chemometric analysis
Partial Least Squares regressions (PLS) were performed with the software Matlab 7.5 on all the
collected spectral arrays, in order to test the ability of the proposed model to quantify the
different concentrations of copper content.
The procedure included the following steps: 1) extraction of raw spectra (X block variables); 2)
extraction of measured values (Y block variables); 3) random separation of dataset into two
subsets, one for the model (75% of the whole dataset) and one for the external validation test
(25% of the whole dataset); 4) application of pre-processing algorithms to both X and Y; 5)
application of the chemometric technique PLS (Partial Least Square): modelling and testing; 6)
calculation of efficiency parameter of prediction.
The partial least squares method is a soft-modelling method (Wold et al., 2001) for constructing
predictive models when the factors are many and highly collinear. The model works through a
specific algorithm (SIMPLS) on the whole array variables (input variables, X-block) and on the
observed values (Y variables) after pre-processing treatments. The model determines the
minimum set of the n estimation variables (LV, latent variables) by a recursive process. These
variables could be represented in an n-dimensional space and they are used by PLS to calculate
the best regression matrix between the X and the Y. PLS allows a model to be calculated that
was tested on external samples observing its prediction ability. The calibration models were also
validated using full cross-validation, Venetian blind.
The model includes a calibration phase and a validation phase calculating for both the residual
errors (Root mean square error in calibration [RMSEC] and in cross validation [RMSECV]). The
prediction ability of the test depends on the number of the LV used in the model and was
performed by means of statistical parameters such as RMSE (root mean square error), the SEP
(standard error of prevision), the correlation coefficient (r) between observed and the predicted
values. The values of r were taken into consideration to study the correlation between the
reference data and the spectral model. Generally, a good model should have high r, with low
RMSE and SEP values. Therefore, the model was chosen with the minimum number of LV that
determines the highest value of correlation between predicted and measured which presents the
minimum SEP value.
F. Antonucci, P. Menesatti, E. Canali, S. Giorgi, A. Maienza, S.R. Stazi. “Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper Concentration”. CIGR Workshop on Image Analysis in Agriculture, 26-27. August 2010, Budapest. 3. RESULTS AND DISCUSSION
Table 1 shows the results of the PLS prediction. While both spectrophotometers, coupled with PLS data analysis permitted rapid estimation of copper concentration, the VIS-NIR was preferable. For VIS-NIR R2 was equal to 0.9456 for the independent test considering 8 LV (Table 1). The model had also low error values (SEP = 69.67 and RMSE =118.81) in the independent test. For the X-block baseline pre-processing was used, while for the Y-block autoscale. Figure 1 shows the correlation between observed and predicted values of copper concentration (mg/kg) for the VIS-NIR analysis in the test represented by the 25% of the whole sample dataset randomly extracted. For NIR a R2=0.8206 for independent test indicated a less accurate model (Table 1). For the X-block Savitsky-Golay weighting were required pre-processes, while for the Y-block autoscale was used. The SEP and RMSE values were higher than those observed for VIS-NIR models. Figure 2 shows the correlation between observed and predicted values of copper concentration (mg/kg) for the NIR analysis in the test. This method investigates whether the system can help in explaining heavy metal interactions with other soil properties. Elevated levels of Cu in European agricultural soils result from the use of Cu-containing compounds to control plant diseases and from applications of manure or sewage sludge. The results indicate that VIS-NIR hyperspectral imaging has the potential to be developed, through appropriate engineering design, into a tool for optimised soil and land management, perhaps in conjunction with plant sensing systems as developed by Menesatti et al. (2010). 4. CONCLUSIONS
This preliminary study lead to significant benefits for soil remediation, survey and precision farming, representing a rapid and non-destructive alternative to classical soil analysis for the detection and characterization of copper topsoil concentration. Even if such systems are less accurate than conventional soil analysis techniques, proximal sensors will facilitate the collection of larger amounts of spatial and temporal data because they are less costly, require less time (Viscarra Rossel and McBratney, 1998) and can build data set of multiple properties with the same sample support that will facilitate understanding of soil system processes. F. Antonucci, P. Menesatti, E. Canali, S. Giorgi, A. Maienza, S.R. Stazi. “Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper Concentration”. CIGR Workshop on Image Analysis in Agriculture, 26-27. August 2010, Budapest. icted
red
P
Observed Cu values (mg/kg)
Figure 1. Correlation between observed and predicted values of Cu in the test-set observed for the VIS-NIR spectral analysis (i.e. 25% of whole sample dataset). (m
s
e
lu
a
v
Observed Cu values (mg/kg)
Figure 2. Correlation between observed and predicted values of Cu in the test-set observed for the NIR spectral analysis (i.e. 25% of whole sample dataset). F. Antonucci, P. Menesatti, E. Canali, S. Giorgi, A. Maienza, S.R. Stazi. “Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper Concentration”. CIGR Workshop on Image Analysis in Agriculture, 26-27. August 2010, Budapest. Table 1. Results of PLS prediction of the Cu values for VIS-NIR and NIR. Parameters VIS-NIR
Pre-processing X-Block
Pre-processing Y-Block
R2 (observed vs
predicted)
R2 (observed vs
predicted)
5. REFERENCES
Ben-Dor, E., Irons, J.R. and G.F. Epema. 1999. Soil reflectance. Remote Sensing for the Earth Sciences, Manual of Remote Sensing. Wiley & Sons Inc., New York, pp. 111–188. Gomez, C., Viscarra Rossel, R.A. and A.B. McBratney. 2008. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: an Australian case study. Geoderma 146:403-411. Janik, L.J., Merry, R.H. and J.O. Skjemstad. 1998. Can mid infra-red diffuse reflectance analysis replace soil extractions? Australian Journal of Experimental Agriculture 38(7):681– 696. Lagacherie, P., Baret, F., Feret, J.-B., Madeira Netto, J. and J.M. Robbez-Masson. 2008. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment 112:825-835. Menesatti, P., Antonucci, F., Pallottino, F., Roccuzzo, G., Allegra, M., Stagno, F. and F. Intrigliolo. 2010. Estimation of plant nutritional status by Vis-NIR spectrophotometric analysis on orange leaves [Citrus sinensis (L) Osbeck cv Tarocco]. Biosystems Engineering 105:448-454. Shepherd, K.D. and M.G. Walsh. 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66:988-998. Viscarra Rossel, R.A. and A.B. McBratney. 1998. Soil chemical analytical accuracy and costs: implications from precision agriculture. Australian Journal of Experimental Agriculture 38:765-775. Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J. and J.O. Skjemstad. 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131:59–75. F. Antonucci, P. Menesatti, E. Canali, S. Giorgi, A. Maienza, S.R. Stazi. “Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper Concentration”. CIGR Workshop on Image Analysis in Agriculture, 26-27. August 2010, Budapest. Viscarra Rossel, R.A., Cattle, S.R., Ortega, A. and Y. Fouad. 2009. In situ measurements of soil colour, mineral composition and clay content by VIS–NIR spectroscopy. Geoderma 150(3–4):253–266. Wold, S., Sjostrom, M. and L. Erikssonn. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory System 58:109-130. F. Antonucci, P. Menesatti, E. Canali, S. Giorgi, A. Maienza, S.R. Stazi. “Hyperspectral Imaging Characterization Of Agricultural Topsoil Copper Concentration”. CIGR Workshop on Image Analysis in Agriculture, 26-27. August 2010, Budapest.

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