color image processing approach for nitrogen estimation of vineyard

International Journal of Agricultural Science and Research (IJASR) ISSN 2250-0057 Vol. 3, Issue 3, Aug 2013, 189-196 © TJPRC Pvt. Ltd.

COLOR IMAGE PROCESSING APPROACH FOR NITROGEN ESTIMATION OF VINEYARD ANUP VIBHUTE1 & S. K. BODHE2 1

Assistant Professor in Electronics & Telecommunication Engineering, BMIT, Solapur, Maharashtra, India 2

Director, Bosh Technology, Pune, Maharashtra, India

ABSTRACT Plant nutrients in proper proportion keep the plant healthy and it is less susceptible to the pests. The nutrient analysis by invasive and non invasive methods has their own advantages and disadvantages. Traditional methods are time consuming where as non invasive methods have proved its importance in recent years. This paper proposes method using color image analysis for estimation of nitrogen for grapes. The function used to estimate shows significant correlation and gives the coefficient of determination of 0.89 with mean square error of 0.08935. The proposed method has advantage of time and cost effectiveness compared to conventional methods.

KEYWORDS: Color Image Processing, RGB Functions, Regression Analysis INTRODUCTION Machine vision or Image processing for agriculture applications were reviewed by many researchers. Machine vision sensing techniques for automated farm operations such as RGB imaging, Stereo vision, multispectral and range sensing were reviewed (McCarthy et al., 2010). The application of Image processing with Imaging techniques, weed detection, fruit sorting, etc. was also reviewed (Vibhute et al,2012; Razali 2011). Image processing is a non destructive method for various agricultural applications that can be effectively implemented with greater accuracy. For chlorophyll content, disease severity and leaf area measurement a survey was reported. Leaf area measurement techniques viz. grid counting, paper weighing, leaf area meter, digital image analysis using different techniques were reviewed. Naked eye observation, image processing techniques like chain code, bounding box, segmentation were reviewed for disease severity along with different chlorophyll content measurement (Aglave et al., 2012). The plant requires macro and micronutrients for optimal growth and development. Climate, crop species, soil type are the major factors affecting nutrient requirements. Visual deficiency symptoms, soil test, plant tissue test, and crop responses to chemical fertilizers or organic manures are the four methods of identifying nutrient disorders in crop plants. Nitrogen is one of the important macro elements required for the growth of the plant which is yield limiting nutrient (Fageria, 2009) . Different non destructive handheld meter techniques such as leaf color chart, SPAD meter, N-tester, Image analysis were evaluated (Ali et al , 2012). Color image analysis is became popular and cost effective method for chlorophyll and nutrient estimation. Many researchers adapted the image processing techniques for color foliage analysis. Digital imaging software like scion was used for analysis and quantification of chlorophyll using leaf color (Murakami et al., 2005). An algorithm was developed to estimate chlorophyll using video camera in which it was shown that the red and blue elements were highly correlated with it. Normalized difference of red and blue was effectively used to estimate chlorophyll under different metrological conditions (Kawashimaet al, 1998).

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Anup Vibhute & S. K. Bodhe

Neural network was developed using R, G and B values for chlorophyll estimation. The results shows high coefficient of determination (R2) and low mean square error where the estimated values were compared with chlorophyll SPAD meter (Moghaddam et al., 2011). The Correlation between vegetation indices and nitrogen leaf content and dry matter at different stages for fertilization was calculated with IR camera and digital camera. The study reveals that the high positive correlation decreases as the number of days increases after fertilization and green spectral band is more useful for nitrogen discrimination (Silva Júnior et al.). Nitrogen estimation for sugarcane using image processing parameters such as R,G, B and their ratios. IR index was also calculated and considered for nitrogen estimation with improved performance (Auearunyawat et al, 2012). Similar kind of experimentation was carried out for nitrogen estimation of sugar beet leaf using image processing parameters and neural network. Estimation was done by neural network and linear regression method and evaluated with SPAD results. Different combinations of R, G, B functions were used for regression analysis. Neural network showed better performance than regression analysis method (Moghaddam et al., 2010). For pepper in flowering and fruiting nitrogen estimation using color image processing was carried out with different functions of RGB. Regression analysis for inorganic nitrogen in soil, total nitrogen, nitrogen concentration and SPAD readings were done and all shows negative coefficient for the considered function (Yuzhu et al., 2011). Reflectance spectral characteristics were used for chlorophyll assessment. Multivariate analysis, regression analysis used for developing calibration model for estimating chlorophyll index for tea plant (Xiao-li Li et al., 2008). Spectral responses of Corn for weed control and nitrogen application rates were studied and classified using support vector machines and compared with other classifier such as neural network (Karimi et al., 2006). CIELAB parameters were used to correlate nitrogen stress in broccoli plants. Relation between b and nitrogen which is measured by chemical analysis was used for determining the nitrogen status. Images were in the range of spectrum 510 1nd 516 nm (Graeff et al., 2008). Image analysis was used using spectral and IR cameras for developing fully instrumented vineyard. The sizes of the berry, canopy, etc were discriminated using image analysis process (Fuentes et al.). Multilayer perceptron and radial basis function neural networks were deployed for leaf nitrogen estimation of corn using conventional spectrums and derived band index. Radial basis function showed optimized output among all the neural network models (Gautam et al., 2007). R, G, B color index values are correlated with nitrogen and chlorophyll of tomato which were measured by chemical analysis and SPAD meter respectively. Red and Blue index showed high negative correlation compared to Green index. SPAD correlation was not recommended by many researchers as it not only depends on nitrogen but also temperature (Mercado-Luna et al., 2010). HIS and CIELAB models were proposed for deficiency diagnosis in three species. The Euclidean distance calculated between HIS for deficient and controlled plants which revealed accurate determination of the deficiencies (Wiwart et al., 2009). Greenness indices based on principal component analysis were used to evaluate nitrogen status of barely plants. The indices showed better correlation than SPAD meter (Pagola et al., 2009). Images converted to one dimensional signal and then processed using wavelet to analyze nitrogen stress showed high correlation for corn. Wavelet coefficients obtained from signal band were evaluated with SPAD meter readings (Reum et al., 2007). Improved hue, luminance and saturation color space which is less susceptible to illumination variation was used to estimate the nitrogen for tomato seedlings. Hue showed better statistical relation with nitrogen values obtained from chemical analysis (Mata-Donjuan et al., 2012). Dark green color index to determine nitrogen for cotton, calculated by two methods: in field imaging and off field imaging. Off field imaging showed better results compared to infield (Raper et al., 2012). Normalized difference vegetation index was calculated for

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Color Image Processing Approach for Nitrogen Estimation of Vineyard

unmanned aerial vehicle images and on ground images. Aerial image index gave better performance compared to on ground as aerial images remove the impact of soil and shadow (Agüera et al., 2011). This paper proposes the method to estimate the nitrogen on the basis RGB color images with RGB plane functions.

METHOD AND MATERIAL 

Image Acquisition Images of grape leaf of different varieties/ rootstock were collected from various farms in Solapur in India. Leafs were

cleaned to remove dust and chemical particles from its surface. Leafs were collected in veraison and after April pruning. The fifth leaf was collected as a sample from different plants in each field. Images are captured on black background during early morning and late evening sessions so as to reduce illumination effect. Images are captured with Nikon Coolpix S570 digital camera having CCD sensor. The resolution is kept at 3 megapixels and images were captured without flash. Camera is positioned at the height of 7-8 inches so that leaf blade can be covered properly. 

Laboratory Analysis Petioles collected from the samples were analyzed in the laboratory. The method used for estimation was Kjeldahl

digestion method. 

Image Analysis Images captured are cropped for getting the leaf blade area as a part for processing. R,G, B planes are separated along

with HSV planes and compared with different combinations of functions obtained from these planes. The different functions used for analysis are as shown in table1. The index that has been already proposed Ik[7] is also considered for comparison. Image I is given by I= f (R,G,B)

(1)

Parameters obtained by image processing are used to correlate with the nitrogen values obtained from laboratory. Linear and non linear regression analyses are applied to obtain optimized regression equation. The results are as shown in table 2. The scattered plot of the variables is as shown in figure 1 and regression graph is as shown in the figure 2. Table 1: Shows the Correlation between the Functions and Nitrogen Function (R-B)/(R+B) 2G*(R-B)/(R+B) 2R*(G-B)/(G+B) 2B/(R+G+B) (R+G)/B (R+2G)/B (R-B)/G (R-B)/(R+G+B)

R -0.6832 -0.6725 -0.6617 0.6705 -0.6766 -0.6655 -0.6836 -0.6837

Improved R -0.8344 -0.8319 -0.8307 0.8338 -0.8409 -0.8328 -0.8262 -0.8314

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Table2: Nitrogen Estimation (R-B)/G 0.168264 0.544021 0.162533 0.194106 0.340613 0.156881 0.157398 0.185847 0.155422 0.209948 0.155923 0.204836 0.161264 0.174635 0.19687 0.168517 0.017265 0.192569 -0.06376 -0.0499 -0.09113 -0.1009 -0.13033

Figure 1: Scattered Plot of Nitrogen (Lab) and (R-B)/G

Ni Lab 0.79 0.23 1.31 1.1 0.66 0.99 1.05 1.34 0.95 0.9 0.92 0.9 0.73 0.84 0.73 0.78 1.46 1.23 1.73 1.18 1.62 1.68 1.18

Ni_est 1.00 0.23 1.01 0.96 0.69 1.02 1.02 0.97 1.02 0.93 1.02 0.94 1.01 0.99 0.95 1.00 1.22 0.96 1.32 1.30 1.35 1.36 1.38

Figure 2: Fitted Curve for the Data

Total 27 field samples were collected and analyzed, after statistical analysis five points which were out of the boundary are eliminated. The statistical analysis is done with curve fitting tool of Matlab.

RESULTS AND DISCUSSIONS Blue plane of images shows the significant positive correlation where as red and green shows negative correlation. After experimentation with various functions of R, G and B almost all functions showed significant correlation with nitrogen value about -0.66 to -0.68. The function 2B/(R+G+B) shows positive correlation with nitrogen. After the statistical analysis with all samples it was observed that few sample exclusion improves correlation coefficient.. After excluding these samples the correlation coefficient improved by 24 percent to -0.83 as shown in table 1.


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Linear and non linear regression analysis options were applied to above for estimation shows that (R-B)/G gives better accuracy compared to (R+G)/B. The regression analysis equation obtained for (R-B)/G using smoothing with moving average at the span of 5. The regression equation is as shown in equation 2. (2) This regression gives the best fitted curve having R 2 of 0.8945 and RMSE of 0.08425. The nitrogen estimated with above equation shows the average error of -3.95 percent for all the samples. Estimated nitrogen shows the correlation with RGB function having coefficient factor -0.99.

CONCLUSIONS Images acquired in natural illumination/condition shows significant correlation with the nitrogen and estimation of nitrogen can be done with better accuracy reducing the time and cost. The method proposed is applied in general to various rootstocks and can be made specific for various rootstocks with little moderations. This will be helpful for the grape growers to estimate the fertilizer requirements at early stage.

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