Analysis of the Leaf Histogram with HSV-Model

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Volume-6, Issue-2, March-April 2016 International Journal of Engineering and Management Research Page Number: 64-68

Prof. Dr. P.K.Srimani1, K. Nithiyanandhan2 1Director R&D Division, B.U., DSI, Bangalore, Karnataka, INDIA 2Department of Master of Computer & Applications, Brindavan College, Bangalore, Karnataka, INDIA and Research Scholar, Rayalaseema University, Kurnool, Andhra Pradesh, INDIA

ABSTRACT results of the present research work include the analysis of Image processing has been proved to be an effective various medicinal leaves through histogram by using the tool for analysis in various fields and applications. The HSV- Model which describes colors in terms of Hue, present study deals with the analysis of medicinal plants like Saturation and Value (brightness). The results are very Peepal Leaf, Betel leaf, Hibiscus Leaf, Karpooravalli etc. by informative. using the techniques of Image Processing. Any disturbance between the elements of human body leads to disease and the Keywords--- Image Processing, Peepal Leaf, Betel Leaf, therapy lies in restoring the balance through the use of Hibiscus Leaf medicinal medicines of natural origin such as herbs and minerals. India is endowed with a rich variety of medicinal plants. The

I. INTRODUCTION Introduction to Medicinal Plants: India is a Image processing is a method to convert an country known for ancient scripts, the number system, image into digital form and perform some operations on it, invention of zero and Vedas. Indian Medicines are used by in order to get an enhanced image or to extract some useful about 60 per cent of the world's population. These are not information from it. It is a type of signal dispensation in only used for primary health care not just in rural areas in which input is image, like video frame or photograph and developing countries, but also in developed countries as output may be an image or characteristics associated with well where modern medicines are predominantly used. that image. Usually Image Processing system includes While the traditional medicines are derived from medicinal treating images as two dimensional signals while applying plants, minerals, and organic matter, the herbal drugs are the regular signal processing methods to them. prepared from medicinal plants only. Use of plants as a It is among rapidly growing technologies today, with its source of medicine has been an ancient practice and is an applications in various aspects of business. Image important component of the health care system in India. In Processing forms core research area within engineering the Indian systems of medicine, most practitioners and computer science disciplines too. formulate and dispense their own recipes; hence this Image processing basically includes the following requires proper documentation and research. In west also three steps: the use of herbal medicines is growing with approximately · i) Importing the image with optical scanner or by digital 40 per cent of populations have report the use of herb to photography. treat medical diseases. During the last two decades general · ii) Analyzing and manipulating the image which includes public, academic and government interest in traditional data compression and image enhancement and spotting medicines is growing rapidly due to the increased side patterns that are not the visible to human eyes like satellite effects of the adverse drug reactions and cost factor of the photographs. modern system of medicine. In rural India, 70 per cent of · iii) Output is the last stage in which visible result can alter the population depends on the traditional type of medicine, the image or report that is based on image analysis.

error (RMSE) between predicted chlorophyll and chlorophyll measured by CM1000 meter is found to be II. RELATED WORK 1.9334. The paper [6] proposes an efficient computer- The review article [1] emphasizes that on the aided Plant Image Retrieval method based on plant leaf detection of plant disease is a very important factor to images using Shape, Color and Texture features intended prevent serious outbreak. Automatic detection of plant mainly for medical industry, botanical gardening and disease is an essential research topic. Most plant diseases cosmetic industry. Here, they use HSV color space to are caused by fungi, bacteria, and viruses. Fungi are extract the various features of leaves. Log-Gabor wavelet identified primarily from their morphology, with emphasis is applied to the input image for texture feature extraction. placed on their reproductive structures. Then green pixels are masked and removed using specific threshold value, III. PROBLEM SPECIFICATION then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. from The main objectives of the present study is to SGDM matrices. Finally the presence of diseases on the make a detailed analysis of same Indian Medicinal leaves plant leaf is evaluated. in particular Peepal Leaf, Betel Leaf, Karpooravalli, The review article [2] on Leaf color is usually Hibiscus etc by using the techniques of image processing used as a guide for assessments of nutrient status and plant methodologies. Different samples are taken and the health. The propose a new inexpensive, hand-held and experiments are conducted. easy-to-use technique for the detection of chlorophyll content and foliar nitrogen content in plants based on leaf IV. METHODOLOGY color. The algorithm produced superior correlations with the true value of foliar chlorophyll content measured in the In order to make a detailed analysis of the leaves, laboratory when compared with existing non-destructive the following steps are performed and the codes are written methods. in Matlab 7.50 Version. In [3] a review article on Agriculture diseases the ALGORITHM science or practice of farming, including cultivation of the Step 1: Read the image soil for the growing of crops and the rearing of animals to Step 2: Convert into RGB to HSV provide food, wool, and other products. Nitrogen is one of Step 3: Set Image the abundant mineral which plays important role in yield h = hsv(:,1); of crops. The paper aims to introduce software “Nitrate s = hsv(:,2); app”. The software has revolutionized the method to find v = hsv(:,3); the nitrogen content in leaves. Step 4: Finds location of black and white pixels In [4] studies on the Plant pathogens which Step 5: Gets the number of all pixels for each cause disease in plants. Chili, peppers are one of the most color bin important crops is presented in the world. The images are Step 6: To find the number of pixels converted to perceptual spaces [hue, saturation and Step 7: Plots histogram lightness (HSL), hue, saturation, and intensity (HSI) and hue saturation and value (HSV)]. HSI color space was the Figure 1 Code for the analysis of leaves best detected disease. The percentage of disease in the leaf is of 12.42%. HSL and HSV do not expose the exact area Primary Colors: The concept of primary colors of the disease compared to the HSI color space. Finally, is based on the fact that any color can be created by images were analyzed and the disease was known by the combining three colors, white, and black. However, since expert in plant pathology to take were understood colors can be generated directly (e.g., by a display) or by preventive or corrective actions. reflection (e.g., by paint), there are more than one In [5] a study on image analysis is discussed for definition in which set of colors are the "primary colors". the determination of chlorophyll content of leaves of In fact, there are three sets of primary colors. sugarcane plant using the HSV color space. A Liner Hue: A hue, or a "pure" color, is the combination mathematical HSV model is proposed to co-relate with the of two primary colors, where one of the two primary colors chlorophyll content, apart from the simple correlation is at full intensity. The discussion of tinting, shading, and analysis. Among the mean HSV (Hue, Saturation, and tones will make this concept more clear.(Figure 1) Value), the significant co-relation is observed between Saturation and Value parameter with chlorophyll content, while no co-relation is observed with Hue parameter. A good agreement between the predicted and actual chlorophyll content is demonstrated. The root mean square 65 Copyright © 2016. Vandana Publications. All Rights Reserved.

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tinting. Thus desaturated colors have increasing total intensity. For this reason, the CSS3 standard plans to support RGB and HSL but not HSV. HSL: The HSL model describes colors in terms of hue, saturation, and lightness (also called luminance). (Note: the definition of saturation in HSL is substantially different from HSV.) The model has two prominent properties: • The transition from black to a hue to white is symmetric and is controlled solely by increasing

Figure 1. Color Wheel lightness Shading and tinting are controlled by a RGB Color Wheel: One of the interesting single value, lightness aspects of the mind's interpretation of color is that it sees • Decreasing saturation transitions to a shade of the relationship between colors in a circular manner. A gray dependent on the lightness, thus keeping the color wheel is a tool that provides a visual representation overall intensity relatively constant of the relationships between all possible hues. The primary Tones are controlled by a single value, hues are arranged around a circle at equal (120 degree) saturation intervals. (Warning: Color wheels frequently depict "Painter's Colors" primary colors, which lead to a different set of hues than additive colors.) RGB: The RGB model's approach to colors is important because: • It directly reflects the physical properties of "True color" displays Figure 2 HSV: The HSV, or HSB, model describes colors in terms of hue, saturation, and value (brightness).Hue corresponds While it's helpful to denote how much of each directly to the concept of hue in the Color Basics section. color exists, it is not a very friendly system to describe a The advantages of using hue are hue shift, saturation, or value/brightness). One has to try • The relationship between tones around the color looking at a color and try to arbitrarily dictate how much circle is easily identified of each primary color composes it. (Figure 2) HSV: A color system that describes a hue shift, saturation, • Shades, tints, and tones can be generated easily without and value is known as HSV. (Figure 3) affecting the hue Saturation corresponds directly to the

concept of tint in the Color Basics section, except that full saturation produces no tint, while zero saturation produces white, a shade of gray, or black. Value corresponds directly to the concept of intensity in the Color Basics section. • Pure colors are produced by specifying a hue with full saturation and value • Shades are produced by specifying a hue with full Figure 3 saturation and partial value If one wants a color to be more turquoise? or • Tints are produced by specifying a hue with partial saturation and full value Scarlet? Plum? Just shift the hue slider until it hits the sweet spot. The brightening will be done without losing • Tones are produced by specifying a hue and saturation. partial saturation and value Natural Brightness: With colors having different natural • White is produced by specifying zero saturation bright nesses to each other, preserving luminosity when and full value, regardless of hue tinting with hue shifts poses a problem with contrast. • Black is produced by specifying zero value, When adjusting a saturation value in HSV, the value scale regardless of hue or saturation adjusts proportionately to maintain the same amount of • Shades of gray are produced by specifying zero brightness saturation and partial value The advantage of HSV is that each of its attributes V. EXPERIMENTS AND RESULT corresponds directly to the basic color concepts, which

makes it conceptually simple. The perceived disadvantage The experiments to analyze the leaves of of HSV is that the saturation attribute corresponds to medicinal plants are conducted by using Matlab (Version 66 Copyright © 2016. Vandana Publications. All Rights Reserved. www.ijemr.net ISSN (ONLINE): 2250-0758, ISSN (PRINT): 2394-6962

7.5). In order to study the color characteristics and histogram, six medicinal leaves viz, Hibiscus, Peepal, Karpooravalli, Eucalyptus, Betel and Tulasi are considered. In the Image Processing technique, HSV- Model which depicts color characteristics through Hue, Saturation and Value (brightness) is considered. The results are presented in Figure 4-9 and Table 1.

VI. CONCLUSION of Biotechnology Vol. 12(7), pp. 679-688, 13 February, 2013. For the past few decades a good percentage of [5] Sanjay B. Patil,and and Sujit S. Patil “Measurement of world’s population is using Indian and herbal medicines, Sugarcane Leaf Chlorophyll “International Journal of because they are side effects free and very economical. Application or Innovation in Engineering & Management India is the largest producer of medicinal herbs (IJAIEM) Volume 3, Issue 2, February 2014 ISSN 2319 – and has a wonderful potential of medical system. 4847. Therefore, the standardization of herbs, medicines and [6] B.Sathya Bama S.Mohana Valli S.Raju V.Abhai manufacturing techniques are absolutely necessary. Kumar , “Content based leaf image Therefore, in the present study, six medicinal leaves viz., retrieval (CBLIR) using shape, color and texture features Hibiscus, Peepal, Eucalyptus, Karpooravalli, Betel and “Indian Journal of Computer Science and Engineering Tulasi are taken for the analysis and the experimental (IJCSE) ISSN : 0976- 5166 Vol. 2 No. 2 Apr-May 2011. results using Matlab(version 7.5) are presented. HSV- Model for the color analysis is considered. The display of colors and their characteristics are presented in Figure 4-9 and Table 1. The results clearly indicate the variations in different leaves through histograms and finally it is concluded. That through Image processing techniques it is possible to explore the hidden scientific potentiates & characteristics in medicinal leaves where for naked eye it is not possible.

VII. ACKNOWLEDGEMENTS

One of the authors Mr. K.Nithiyanandhan acknowledges Brindavan College Department of Master of Computer Applications, Bangalore, Karnataka and Rayalaseema University, Kurnool, India for providing the facilities for carrying out the research work.

REFERENCES

[1] Sanjay B. Dhaygude, and Nitin P.Kumbhar, on “Agricultural plant Leaf Disease Detection Using Image Processing” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 1, January 2013 ISSN: 2278 – 8875 [2] Mahdi M. Ali, Ahmed & Al-Ani, Derek Eamus and Daniel K.Y. Tan on “A New Image Processing Based Technique to Determine Chlorophyll in Plants”, A meri can- Eurasian J. Agric. & Environ. Sci., 12 (10): 1323-1328, 2012 ISSN 1818-6769. [3] Vasudev B. Sunagar, Pradeep A.Kattimani, Vimala A. Padasali,Neetha and V. Hiremath “Estimation of Nitrogen Content In Leaves Using Image Processing” Proceedings of International Conference on Advances in Engineering & Technology, 20th April-2014, Goa, India, ISBN: 978-93- 84209-06-3. [4] J. L. González-Pérez1 , M. C. Espino-Gudiño, J. Gudiño-Bazaldúa, J. L. Rojas-Rentería, V. Rodríguez- Hernández and V.M. Castaño “Color image segmentation using perceptual spaces through applets for determining and preventing diseases in chili peppers” African Journal