Estimation of Nitrogen Content by Spectral Responses of Cabbage Seedlings Using Artificial Neural Network Approach

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1 An ASAE/CSAE Meeting Presentation Paper Number: Estimation of Nitrogen Content by Spectral Responses of Cabbage Seedlings Using Artificial Neural Network Approach C. T. Chen, Graduate Student; S. Chen, Professor Dept. of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan. K. W. Hsieh, Associate Professor Dept. of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung Taiwan. H. C. Yang; Assistant Researcher; S. Hsiao, I C. Yang, Graduate Student Dept. of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan. Written for presentation at the 2004 ASAE/CSAE Annual International Meeting Sponsored by ASAE/CSAE Fairmont Chateau Laurier, The Westin, Government Centre Ottawa, Ontario, Canada 1-4 August 2004 Abstract. Reflectance spectra of leaves are informative for non-destructive monitoring of the nutrition status. Nitrogen content of cabbage seedling leaves cultivated with five different fertilization conditions was measured by Kjeldahl method, and was correlated with spectrum reflectance. Since the wavelength of the band-pass filters for silicon CCD multi-spectral imaging was shorter than 1000nm, a selected band ( nm) was considered in addition to the full band ( nm). Step-wise multi-linear regression (SMLR) and artificial neural network (ANN) were used to evaluate the effectiveness of the wavelength in determination of nitrogen content. The analytical results of SMLR calibration equations with four significant wavelengths (566, 574, 1396, and 1530 nm; r c 2 =0.82, SEC=9.85 mg/g, and SEV=12.35 mg/g) were significantly improved by an ANN model (r c 2 =0.89, SEC=8.27 mg/g, and SEV=8.84 mg/g) with cross-learning and random- sampling strategies, in which the over-fitting was greatly reduced. For developing a practical multi-spectral The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of ASAE or CSAE, and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process, therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASAE/CSAE meeting paper. EXAMPLE: Author's Last Name, Initials Title of Presentation. ASAE/CSAE Meeting Paper No. 04xxxx. St. Joseph, Mich.: ASAE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASAE at hq@asae.org or (2950 Niles Road, St. Joseph, MI USA).

2 imaging system with commercially available band-pass filters, the ANN model with four inputs of 490, 570, 600, and 680 nm was trained to obtain a comparable result (r 2 c =0.87, SEC=8.73 mg/g, r 2 v =0.84, and SEV=9.60 mg/g) to the best calibration equation of SMLR with seven wavelengths in the full band. The potential of using the ANN model developed in this study with multi-spectral imaging to monitor the nitrogen content of cabbage seedling is expected. Keywords. Artificial neural network, Cross-learning, Nitrogen content, Reflectance spectra (The ASAE/CSAE disclaimer is on a footer on this page, and will show in Print Preview or Page Layout view.) The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of ASAE or CSAE, and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process, therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASAE/CSAE meeting paper. EXAMPLE: Author's Last Name, Initials Title of Presentation. ASAE/CSAE Meeting Paper No. 04xxxx. St. Joseph, Mich.: ASAE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASAE at hq@asae.org or (2950 Niles Road, St. Joseph, MI USA).

3 Introduction Nitrogen content is crucial for the growth of plants; direct and non-destructive remote sensing of their nutrient status is promising especially in cultivating the crop (Goel et al., 2003; Boegh et al., 2002; Diker and Bausch, 2003). Nitrogen contents of plants in the form of dry powder, fresh leaves and even the canopy have been estimated by calibrating the reflectance spectra with the results of wet-chemical analysis (White et al., 2000; Grossman et al., 1996). Typically, step-wise multi-linear regression (SMLR) and modified partial least square regression (MPLSR) were applied to analyzed the spectra obtained with a spectrophotometer or a fiber optic spectrometer (Yoder and Pettigrew-Crosby, 1995; Serrano et al., 2002; García-Ciudad et al., 1999; Johnson and Billow, 1996; Gillon et al., 1999). SMLR method requires fewer parameters for a satisfactory fitting of calibration equation. Yorder and Pettigrew (1995) successfully estimated the nitrogen contents in maple leaves using photometric data of 3 and 5 different wavelengths, and the determination coefficients (r 2 ) of SMLR were 0.71 and 0.85, respectively. Compared with SMLR, MPLSR method is even better for chemometric applications (Bolster et al., 1996; Gillon et al., 1999), but spectral data of full ( nm) or partial ( nm) bands and therefore a sophisticated optical system with grating mechanism or tunable filters are demanded. The powerful calibration method is therefore inappropriate for remote sensing studies using simple or field-type optical devices such as photodiodes (Stone et al., 1996). Moreover, remote sensing including multi-spectral imaging techniques (Goel et al., 2003; Wood et al., 2003) using simple optics devices provide additional flexibility in eliminating environmental noise that will disturb the chemometric prediction. Therefore, calibration methods using fewer wavelengths (i.e. SMLR) are more suitable for general remote sensing purposes. Wavelength determination is important for the cost reduction and precision of a remote sensing system, but cares should be taken for the non-linear phenomena at the chosen wavelengths. To calibrate a non-linear system, artificial neural network (ANN) with sigmoid transfer function and error back-propagating training scheme was widely used (Liu et al., 2001; Hsieh et al., 2001; Burks et al., 2000). Recently, Hsieh et al., (2002) had developed an ANN classification model for carcass (94% accuracy), but a large number of wavelengths was required. With fewer parameters, Mutanga and Skidmore (2004) compared the ANN and SMLR to map the grass nitrogen concentration in an African savanna rangeland. The ANN results showed a higher correlation coefficient (r= 0.96) for the training set, but the results for the test sets was not satisfactory (r= 0.77). In the present approach for appraising the application potential of multi-spectral imaging system for insitu monitoring the nutrient status of cabbage seedlings, the significant wavelengths were determined by SMLR method, and a new ANN training scheme was proposed for the nitrogen content prediction. The scheme required fewer input nodes for simulating the spectral data extracted from multi-spectral images of the seedlings, and a higher prediction accuracy was obtained. Materials and methods Cultivation of Cabbages Seedlings Cabbage seedlings were planted in plastics pots of 8 cm diameter filled with medium composing of peat moss and perlite (1:1); each pot was irrigated with 50 ml of water every two days, and 2

4 grown in 25 (day) and 20 (night) in a phytotron located in National Taiwan University. After 20 days of the conditioned growing, each pot was fertilized with 50 ml of HYPONeX No. 5 (HYPONeX, N: P: K=30:10:10) diluted to different extents (0, 500, 1000, 2000 and 4000 ppm). The fertilized seedlings were continuously grown until the 25th day. Spectra Acquisitions and Nitrogen Content Measurement After 25 days of cultivation, the foliages (432 from 130 seedlings) were cut and put into the small ring cup (NIRS 6500, FOSS NIRSystems) to measure the reflectance absorbance spectra (Table 1). Total nitrogen contents of 118 randomly selected foliages were measured by Kjeldahl method (García-Ciudad et al., 1999), and the results (TKN values, Table 1) were used to calibrate with different spectra types (smooth, derivative, full and partial bands of raw data) by MPLSR (WinISI software, FOSS NIRSystems). The best MPLSR equation, resulting from comparing rc2, SEC (standard error of calibration) and SECV (standard error of cross validation), was used to predict the nitrogen content of other samples. Determination of multi-spectral imaging filters by SMLR Spectra and nitrogen content of leaves (432 samples) were divided into the calibration set (324 samples) and validation set (108 samples) for SMLR analysis; the samples in both sets were with similar nitrogen content distributions (Table 1). Raw spectra, from nm with 2 nm resolution, were indicated as ( , 2) and used to establish calibration equations with one to seven significant wavelengths. By comparing r2, SEC, and SEV (standard error of validation), the best equation was selected with understanding the capability of error convergence by SMLR. In order to extract the significant wavelengths for determining the filters of multi-spectral imaging system, the spectra in the sensitive band of CCD camera from 450 nm to 950 nm at 2 nm interval, indicated as ( , 2), were also analyzed by SMLR. Since the full width half maximum (FWHM) of commercial band-pass filters are usually 10 or 20 nm, the spectra in nm were reconstructed by 10-points (20 nm) smoothing and selecting data points at 10 nm interval. The reconstructed spectra were indicated as ( , 10) and used to analyze the significant wavelengths to compare the results of the spectra types ( , 2) and ( , 2). The significant wavelengths sets determined by SMLR in three types of spectra (( , 2); ( , 2); ( , 10)) were then used to model the ANN for estimating nitrogen content of leaves to obtain better calibration and prediction. Conventional Modeling of ANN The frame of adopted ANN was a perception multilayer network with error back-propagation as the training scheme and the generalized delta rule for weighting and threshold values adjusting (Fig. 1). The input nodes were the wavelengths determined by SMLR; the numbers of neuron nodes in hidden layer 1 and 2 with the sigmoid transfer function were analyzed from 10 to 25 at 5 interval, and the singular output node with linear transfer function was used to calculate the nitrogen content of leaves. Matlab 6.1 (The Mathworks, Inc.) was used to model the ANN with adjustable learning rate and momentums to converge error quickly at each batch of training. The initial values of weightings and thresholds were set consistent to escape the danger of being trapped in local minima during a gradient-based search (Boger, 2003). The training scheme of ANN used the calibration sample set divided from all samples as the main objects to decrease error of the model and the remaining samples were as the validating objects to 3

5 confirm the situation of over-fitting. Besides, the Savebest strategy (Hsieh et al., 2002) of ANN was also adopted. Therefore, the same sample sets of calibration and validation with SMLR were analyzed to determine the proper number of nodes in hidden layers and to confirm the ability of ANN for converging the error of nitrogen content prediction. Spectral Data Weightings of Neuron in hidden layer 1 Thresholds of Neuron in hidden layer 1 Sigmoid transfer function Weightings of Neuron in hidden layer 2 Thresholds of Neuron in hidden layer 2 Sigmoid transfer function Weightings of Neuron in output layer 2 Thresholds of Neuron in output layer 2 Liner transfer function Nitrogen Content Figure 1. The structure of ANN model with one input layer, two hidden layers, and one output layer for traditional back-propagation training scheme. Proposed Modeling of ANN The proposed cross-learning schemes (Fig. 2) divided the samples of calibration into three subsets. Every two subsets were combined as the learning set in the next step, and each learning set was used as the calibration set in ANN modeling by turns. During the training process, the weighting and threshold values were adjusted by the errors resulting from different learning sets. The model of ANN with cross-learning scheme also validated by validation set and used the Savebest strategy. Finally, based on the conception of cross-learning, partial samples of calibration set were randomly selected into the learning set of ANN to calculate the error for adjusting the weightings and thresholds. The error convergence values of all calibration set were computed and compared with the results of validation set using the mentioned strategy. Therefore, the results of three ANN types were compared at every training epoch for their SEC and SEV values and at the best epoch for the r c 2 and r v 2 values. Sample Set 1 Learning Set 1 If N=3 i Calibration Sample Sample Set 2 Learning Set 2 If N=3 i+1 ANN training Sample Set 3 Learning Set 3 If N=3 i+2 Figure 2. The new scheme of ANN training with cross-learning mechanism detailed in the experimental section. 4

6 RESULTS AND DISCUSSION Nitrogen contents in seedling leaves In Table 1, the total nitrogen content of randomly selected samples (n= 118) were determined by Kjeldahl method (TKN values), and the statistical results were tabulated. These analytical results were used to develop MPLSR calibration equations for the reflectance absorbance spectra of the samples. The spectra ( nm) were smoothed (ten point smoothing) and its second derivative spectra (data interval = 10) was taken to obtain the best calibration equation by MPLSR. The best calibration results were r 2 c =0.97, SEC=4.24 mg/g, SECV=5.23 mg/g. The statistical results of the nitrogen contents obtained by Kjeldahl method and predicted by MPLSR are similar, which indicates the homogeneity of the random sampling process. In order to determine the significant wavelengths for selecting suitable filters of multi-spectral imaging, leaves (432 samples) were divided into calibration (324 samples) and validation sets (108 samples), the distributions of nitrogen contents were similar as revealed by MPLSR prediction (Table 1). The samples were then analyzed by SMLR to determine the significant wavelengths. Table 1. Statistical results of the nitrogen contents (mg/g) of seedling leaves for different experimental groups Number of Mean S.D. Max. Min. sample TKN results of selected samples MPLSR results of all samples MPLSR results for calibration set* MPLSR results for validation set* * The sample sets were used for SMLR and ANN analysis. Number of Signification Wavelengths and SMLR Error in Full Band Table 2 shows the SMLR prediction errors obtained by analyzing one to seven significant wavelengths in full band. The errors were converged until the seventh terms without over-fitting. With seven wavelength calibration, the high linearity and low prediction errors can be achieved without pretreatments of the raw spectra. Table 2. SMLR results of one to seven wavelengths in full band. Number of Wavelengths Calibration set SEC r 2 c Validation set SEV The reported remote sensing with multiple radiometers or spectral imaging system were less than four bands (Stone et al., 1996; Diker and Bausch, 2003; Kostrzewski et al., 2003); four terms were therefore considered to be the maximum for establishing the remote sensing model 5

7 with multi-spectral apparatus. However, as shown in Table 2, the results are not satisfactory, and other modeling methods should be attempted to increase the accuracy. In the following, filter sets of four and seven different wavelengths were determined from their SMLR results calibrated for the spectra types (( , 2); ( , 2); ( , 10)) mentioned in the experimental section. Determining Filters for Multi-Spectral Imaging System Table 3 listed the SMLR regression results of three types of spectra against four and seven selected wavelengths. The calibration equation with 7 wavelengths from full spectral band was the best, but the four wavelengths (1396 nm, 1536 nm, 1868 nm, and 2190 nm) in the nearinfrared region require advanced but expensive imaging devices such as InGaAs or HgCdTe camera to construct a multi-spectral imaging system (Ungar et al., 2003). The applications will be restricted and inappropriate for agricultural purposes. The significant wavelengths ranged from 450 to950 nm are considered to be suitable for general and routine usage. Although slightly less accurate than the full band result, the results of CCD sensing band of four and seven wavelength calibration are acceptable with additional economic benefits for assembling a CCD-based multi-spectral imaging system. The suitable wavelengths were restricted further by commercially available band-pass filters, and calibration equation with spectral data at 490, 570, 600, and 680 nm were found to be satisfactory for quantification purposes. With respect to the last calibration results of the spectra of wider bandwidth (20 nm), the data smoothing pretreatment may help in noise removal and thus improve the prediction accuracy. Table 3. Significant wavelengths and the prediction errors obtained by SMLR analysis. 2 Source spectra Significant wavelengths SEC r c SEV ( , 2) 566, 574, 1396, ( , 2) ( , 10) Error convergence of ANN 566, 596, 686, 1396, 1536, 1868, , 540, 606, , 574,,596, 612, 630, 654, , 570, 600, , 570, 590, 640, 660, 670, ANN with traditional training scheme was performed by dividing all samples into calibration and validation sets, and the data of 4 significant wavelengths from full spectral band were input to analyze the proper network structure of layer and node numbers. Figure 3A shows the training results with one hidden layer, the error converged after 150 training epochs in case of 10 and 15 nodes, but the SEC and SEV show some inconsistency in case of the structure with higher node number. Over-fitting probably occurred in the training course. Similar phenomena were more obvious in the ANN model with two hidden layers (Fig. 3B). Both results revealed a dilemma of the accuracy and consistence between calibration and validation sets, a training scheme based on cross-learning was therefore designed. The proposed training scheme with three alternating fixed learning sets was analyzed with the same structures as the traditional scheme, the results were compared in Fig. 3C and Fig. 3D. The SEV after 1000 training epochs converged to about 12 mg/g with one hidden layer and 10 to 25 nodes (Fig. 3C); over-fitting was reduced but not to a satisfactory extent. The results with 6

8 two hidden layers (Fig. 3D) also shows a reduced divergence in contrast to traditional ANN, but the problematic over-fitting and inconsistent phenomena still existed. Figure 3E and 3F are the results obtained with a similar cross-learning training scheme except that the samples in the learning set were randomly selected from the calibration set. The improvements obtained with every ANN structure are obvious. Regarding the error convergence ability and the training cost, network of two hidden layers with 15 nodes was used in the following. A B C D E F Figure 3. The error convergence of ANN model consisting 4 inputs from full band with 10 (N10) to 25 nodes (N25): (A) one hidden and (B) two hidden layers by traditional training scheme, (C) one hidden and (D) two hidden layers by cross-learning training scheme with fixed learning sets, (E) one hidden and (F) two hidden layers by cross-learning training scheme with randomly selected learning sets. 7

9 Results of ANN models based on different spectral data The aforementioned three learning schemes of ANN were conducted with Savebest strategy to record the best ANN model during the training process. The Savebest strategy is effective to achieve the best results without over-fitting that frequently occurred in a traditional training scheme. Therefore, the strategy was attempted in the present study to obtain the best model from the oscillating convergence characterized by the proposed cross-learning scheme. Figure 4 shows a typical modeling results (SEC and SEV) of 5000 training epochs, the SEV of the proposed model was converged at 8 mg/g. Figure 4. Distributions of SEC and SEV values in 5000 training epochs. ANN models with the same initial values of weightings and thresholds will finally lead to a constant error convergence at certain epochs (Table 4, the last column), but the modeling errors with randomly selected learning sets did not converge at a defined epochs number. As shown in the data rows for the proposed scheme, the standard deviations (as compared with the averages) are in an acceptable range from four repeated modelings. Considering the error convergences at every training epoch of the three ANN training schemes, the third type of ANN with Savebest was suggested according to prediction ability. The SEV obtained with four input nodes from spectra source of ( , 2) was reduced from mg/g (traditional ANN) to 8.84 mg/g (the proposed scheme). As compared with the SMLR result (12.35 mg/g), the improvement is even more significant (28%). The spectra source for calibration model is crucial for developing a practical multi-spectral imaging system in agriculture applications. The ANN model with input data at 566, 574, 1396, and 1530 nm was the best but requires expensive imaging devices as mentioned early. The ANN results with four inputs from the spectra sources of shorter wavelengths, ( , 2) and ( , 10), were more promising for a general multi-spectral imaging applications; the accuracy were both improved 10 % of error convergence as compared with SMLR. The error convergences obtained by the proposed ANN modeling with four inputs were not significantly 8

10 affected by their spectra sources. The choosing of spectral source for the ANN modeling is therefore more flexible for constructing an economic and practical multi-spectral imaging system. The proposed ANN models with seven inputs were also performed. Compared with the SMLR results, the prediction accuracy had significant improvement except for the SEC from the spectra source of ( , 2). Additional input terms until seven terms did increase the accuracy of SMLR analysis without over-fitting problems, but similar effects did not happen to the proposed ANN model. Using non-linear transfer function to fit the error of nitrogen prediction model, the ANN model with four selected input nodes obtained comparable results with seven input nodes. The ANN model with four input nodes from spectra of ( (10)) was suggested for building the remote sensing model, the results, r 2 c =0.87, SEC=8.73 mg/g, r 2 v =0.84, SEV=9.60 mg/g, were satisfactory. Compared with SMLR, the proposed ANN is more effective in error-fitting ability, more flexible in parameter (wavelength) selection. To monitor the nitrogen status of cabbage seedlings, multispectral imaging system with silicon CCD camera and four band-pass filters of 490 nm, 570 nm, 600 nm, and 680 nm is suggested. Table 4. Results of ANN with different training schemes. Spectra source No * ANN type 2 r c SEC 2 r v SEV Epochs ( , 2) 4 ANN a ANN b ANN c 0.89± ± ± ±0.15 ( , 2) 4 ANN a ANN b ANN c 0.87± ± ± ±0.22 ( , 10) 4 ANN a ANN b ANN c 0.87± ± ± ±0.23 ( , 2) 7 ANN a ANN b ANN c 0.88± ± ± ±0.40 ( , 2) 7 ANN a ANN b ANN c 0.89± ± ± ±0.05 ( , 10) 7 ANN a ANN b ANN c 0.87± ± ± ±0.27 * Number of Wavelengths. a Result of ANN with traditional learning scheme. b Result of ANN with cross-learning scheme and fixed learning set. c Average result of ANN with cross-learning scheme and random selecting sample for learning set. Conclusion Significant wavelengths for estimating nitrogen content of cabbage seedling leaves were determined by SMLR analysis, and a modified ANN model was proposed to further increase the prediction accuracy. Wavelengths were selected from several spectra sources including full band spectra, spectra ranged from 450 nm to 950 nm, and spectra limited by commercially 9

11 available filters for multi-spectral imaging using CCD. The SMLR results using parameters of four and seven wavelengths did not reach to linear regression equation with sufficient accuracy. Using the strategies of cross-learning and random-sampling in/between the calibration sets, the new training scheme was proven to be more precise than SMLR and traditional ANN methods. The problematic over-fitting effect of ANN was extensively reduced, and data set with even fewer parameters can be used to obtain satisfactory results from various spectra sources. A multi-spectral imaging system for agricultural purposes can be easily constructed with CCD and the band-pass filters when the proposed ANN model was adopted in the calibration modeling. References Bausch, W. C., and H. R. Duke Remote sensing of plant nitrogen status in corn. Transactions of ASAE 39(5): Boger, Z Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis. Analytica Chimica Acta 490: Boegh, E., H. Soegaard, N. Broge, C. B. Hasager, N. O. Jensen, K. Schelde, and A. Thomsen Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sens. Environ. 81: Bolster, K. L., M. E. Martin, and J. D. Aber Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared: a comparison of statistical methods. Can. J. For. Res. 26: Burks, T. F., S. A. Shearer, R. S. Gates and K. D. Donohue Backpropagation neural network design and evaluation for classifying weed species using color image texture. Transactions of ASAE 43(4): Diker, K., and W. C. Bausch Radiometric filed measurements of Maize for estimating soil and plant nitrogen. Biosystem Engineering 86(4): García-Ciudad, A., A. Ruano, F. Becerro, I. Zabalgoeazcoa, B. R. Vazquez de Aldana, and B. Garcia-Criado Assessment of potential of NIR spectroscopy for the estimation of nitrogen content in grasses from semiarid grasslands. Animal Feed Science and Technology 77: Gillon, D., C. Houssard, and R. Joffre Using near-infrared reflectance spectroscopy to predict carbon, nitrogen and phosphorus content in heterogeneous plant material. Oecologia 118: Goel, P. K., S. O. Prasher, J. A. Landry, R. M. Patel, A. A. Viau, and J. R. Miller Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Transactions of the ASAE 46(4): Goel, P. K., S. O. Prasher, R. M. Patel, D. L. Smith, A. DiTommaso Use of airborne multispectral imagery for weed detection in field crops. Transactions of the ASAE 45(2): Grossman, Y. L., S. L. Ustin, S. Jacquemoud, E. W. Sanderson, G. Schmuck, and J. Verdebout Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sens. Environ. 36: Hsieh, C., Y. R. Chen, B. P. Dey and D. E. Chan Separating septicemia and normal chicken livers by visible/near-infrared spectroscopy and back-propagation neural networks. Transactions of the ASAE 45(2): Hsieh, K. W., S. Chen, W. H. Chang, M. T. Lee and C. T. Chen A dynamic simulation model for seeding growth. Transactions of the ASAE 44(6):

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