Ayurvedic Plant Species Recognition using Statistical Parameters on Leaf Images

Transcription

1 International Journal of Alied Engineering Research ISS Volume, umber 7 (06) Research India Publications. htt:// Ayurvedic Plant Secies Recognition using Statistical Parameters on Leaf Images Pusha BR, Anand C and Mithun ambiar P Deartment of Comuter Science Amrita Vishwa Vidyaeetham, Mysuru Camus Amrita University, India Abstract Automatic recognition of lant secies recognition is a challenging roblem in the area of attern recognition and comuter vision. An efficient lant recognition system will be beneficial to many sectors of society which includes medical field, botanic researches and lant taxonomy study. Manual identification rocess requires rior knowledge and also it is a lengthy rocess. This aer rooses a simle and efficient methodology for Ayurvedic lant classification using digital image rocessing and machine vision technology. The three major hases in roosed methodology are re-rocessing, feature extraction and classification. Pre-rocessing is done in order to highlight the relevant features to be used in the roosed methodology as well as to reduce unwanted noise from the inut image, which reduces the chance of getting otimal feature values. In feature extraction hase, different morhologic features such as mean, standard deviation, convex hull ratio, isoerimetric quotient, eccentricity and entroy are extracted from the re-rocessed leaf image. In the third hase, a new aroach to classify ayurvedic lant secies is adoted to recognize lant secies by calculating the leaf factor of the inut leaf using the extracted feature values and it is comared with the trained values that are stored in the database. An accuracy of 93.75% is obtained for the roosed methodology. Keywords: Feature extraction, Leaf factor, Ayurvedic Leaf Classification ITRODUCTIO Ayurveda is an ancient medicinal system evolved in India around thousands of years ago, still followed by many eole as it is urely natural and has no side effects. It is very relevant from ancient to this most modern time because of its ower to cure chronic diseases. The arts like leaf, flower, root, bark and fruit are mainly used in the rearation of medicines in Ayurveda. At resent, the lants are identified manually by exerienced hysician or taxonomists, which are rone to human errors in many cases. In order to avoid theses human errors, this aer rooses an automated methodology for the identification of medicinal lants which make use of a lant leaf as an inut and the classification of lants and its medicinal values as outut. According to Ayurveda every lant on earth has some medicinal value, so it is imortant to rotect the lant and identify its medicinal values. Studies have roved that consuming so much of alloathic medicines may lead to side effects as it carries out many chemical reactions within the body. A general fact about Alloathy is that once it is taken, it requires taking another medicine to cure the side effects which has haened due to the revious medicine. In general, rocess of consuming medicines will not end. Alloathic treatments are meant to treat the Symtoms of a disease whereas Ayurveda treats the root of the disease. One of the major advantages of Ayurveda is that it does not have any side effects as it is urely natural, that is relevant in this most modern time as new diseases evolve due to changed life style and changed diet. So it is imortant for every human being to return back to Ayurveda. Almost all general diseases can be cured through Ayurveda using the shrubs and herbs that are around us. Ayurveda also brings lots of foreign money to the country since many foreign countries are inclining towards it. Plants are the basic building blocks of life on earth and it is comlex to identify a lant secies through a hoto grah because of its comlex three-dimensional structure which cannot be catured through cameras, but it is ossible if the leaf can be identified. Fortunately most of the leaves are twodimensional and it is ossible to automate the identification of a lant secies through its leaf morhology exloitation It is essential for everyone to rotect the lants as in today s world deletion of lants and trees are haening faster due to urbanization. Medicinal lants can be classified based on its internal as well as external features. The external features such as colour, shae, textures and edge histogram are used as their identification arameters. In this aer, an automated system is roosed to recognize the taxonomy of Ayurvedic lants by extracting features from their leaf images and alying mathematical oeration to obtain the leaf factor, which can be comared with the trained leaf factor that is in the database to match the inut leaf image and classify the Ayurvedic lant secies. RELATED WORK Researchers have tried many methodologies to extract the features and identify the lant secies automatically. Most of these methods make use of combination of many arameters like colour, shae and texture features. Abdul Kadir et.al [] roosed a method to identify the lants and various features like texture, vein, shae and colour of the leaves are extracted. The vein feature is extracted using the morhological oening oeration and robabilistic neural network is used for classification. The aer limits in achieving reliability with resect to colour feature. 54

2 International Journal of Alied Engineering Research ISS Volume, umber 7 (06) Research India Publications. htt:// Abdolvahab Ehsanirad et.al [] roosed a methodology to extract the texture feature of the leaf image and classification. Two different algorithms namely Princial Comonent Analysis Algorithms (PCA) and Gray Level Co-occurrence Matrix (GLCM) are used to achieve an accuracy of 78% in extracting the texture feature. A.J. Pérezet.al [3] used the colour and shae feature of leaf image to discriminate soil, weed, and cros. For the roosed methodology different shae features like ratio of the major axis length squared to the area, first invariant central moment, major axis length, ratio of the erimeter squared to the area, minor axis relation, distance to the cro row are used to discriminate soil and weeds. K-earest eighbor (K), bayes rule and heuristic aroaches are used in classifying the leaf image. An accuracy of 89.7% is acquired from the roosed method. Pande Ankita Vet.al [4] roosed a methodology for fruit tree recognition using the chain code method. The aer introduced a method called Comuter-Aided Plant Secies Identification Technique (CAPSI), which is based on the image matching technique of leaf shae. Different biometric features like width factor, diameter, major axis, minor axis, area, erimeter and asect ratio of the leaf image are extracted. Artificial neural network (A) classifier is used for classification of leaf image. Colhong Lm et.al [5] roosed a system which uses contour of leaves as the main feature to classify the leaf image. In the first ste teeth of the leaf and secondly the global structure of the leaf is calculated. Polygonal aroximation is done to extract the contour of leaves and also used to detect the midmost iece of leaf. For classification, a hierarchical method is used.based on the arameter like aex numbers and similarity measure, the leaf is detected. eto.j et.al [6]carried out an exeriment with four different lant secies namely young soybean, sunflower, red root ig weed,velvet leaf. Ellitic Fourier (EF) and harmonic functions were generated based on the contour of the leaf. Based on the variations between consecutive EF functions and a comlexity,index of the leaf shae is calculated. This methodology achieves an accuracy of 89.4%. James S Coe et.al [7] roosed an efficient methodology for lant classification based on the texture of leaves. Joint distributions for the resonses from alying different scales of the gaborfilterare calculated and the difference in leaf structure is calculated by the Jeffrey- divergence measure of corresonding distributions. This methodology worked well in terms of accuracy. Kue-Bum Lee et.al [8] roosed a Methodology to extract the features of the leaf vein. This work is carried out by finding the contour of the leaf. Firstly by converting the colour image into a gray scale and then to binary image and hence outline of the leaf is extracted. To extract the veins of the leaf, oening oeration is done on the grayscale image and the difference in the final image and gray scale image is obtained to get the features extracted. Sanjay B et.al [9] roosed a methodology in which the inut image is converted from RGB to Hue Saturation Value (HSV) and the green ixels are masked before removing and the comonents are segmented then the useful segments are obtained and finally the colour co-occurrence methodology is used for classification. Sanjeev S Sannakki et.al [0] resented a aer on comarison between different edge detection methods for leaf images. Fuzzy mathematical morhology is used to carry out oening, closing, erosion and dilation on an image. The dilation and erosion oeration can be carried out by the way that the mask is shifted over the image which has been rocessed by a membershi function. Vijay Satti et.al [] describes how features are extracted after re-rocessing. The rocedure involved are re-rocessing, RGB to Gray scale and then Gray scale to binary followed by smoothing and filtering. Finally the colour shae and geometric features are extracted. The aer deals with the disease detection in addy leaves by the aroach of histogram rocessing mechanism. The original disease free leaf is stored in the database and whenever a disease affected leaf image is given as inut to the system, it redicts the amount of disease infected in the leaf by analyzing the histogram. Sandee Kumar. E[] roosed a system with devised methodology which gives the identification of medicinal lants based on its edge features. The colour image is converted to its gray scale equivalent image. From this gray scale image edge histogram is calculated. Canny edge detection algorithm is imlemented in this work. The rocess includes the stages of Image acquisition, feature extraction and comaring the image with those images that are reviously stored on the database and the area of leaf is determined by taking one Ruee coin s area as the reference, which is comaratively effective since the hotograh taken may vary from erson to erson. This work is limited to detect only the mature leaves since the tender leaves changes slightly when it became mature. Ji-Xiang Du et.al [3] roosed a new classification method called Move Median Centres (MMC) hyer shere classifier. From the exerimental results of this aer, the methodology save both storage sace and reduces the classification time. From the above review of literature it is very clear that no effective methods were roosed for Ayurvedic lant secies recognition and hence this roosed system addresses the Ayurvedic lant recognition METHODOLOGY The roosed methodology consists of five stes namely, Image acquisition, re-rocessing, feature extraction, classification training and testing. The flow of the system is deicted in Fig.. 543

3 International Journal of Alied Engineering Research ISS Volume, umber 7 (06) Research India Publications. htt:// angles and orientation.the exeriments are carried over the datasets collected. Pre-Processing Pre-rocessing the images is an imortant ste as it increases the robability of getting desired outut in the future stes of image rocessing. In order to extract the colour feature of the inut leaf, global threshold value of the inut leaf image is calculated which is useful in enhancing the image and thereby makes the feature extraction hase easier. Then the inut image is converted to gray scale and to binary image so as to kee the ixel values as either or 0, so as the feature extraction oerations can be made simler as well as the image gets stored as lower sized binary images, without losing any of its morhological features. Inut image is smoothened in order to reduce the noise in the image. Smoothening reduces the number of ixels in the image and it hels in detecting the edges in an image. There are low ass filters as well as high ass filter to make an image smoother. In the roosed methodology Lalacian filtering is alied for edge detection, which comutes the second order derivatives of an Image. The re-rocessing stes are shown in Fig.. Figure : Block Diagram for Ayurvedic Leaf Recognition System Figure : Stes in Pre-rocessing The leaf recognition is carried out through image rocessing techniques. The leaf image of a articular lant is fed into the system and the system will re-rocess the image in order to reduce the noise resent in it and to obtain gray scale, binary and edge for future extraction. In the feature extraction hase, arithmetic mean on colour image, standard deviation on colour as well as convex hull of the leaf in the image, Entroy on gray scale image, convex hull ratio, isoerimetric quotient and eccentricity is calculated. After the feature extraction hase, the leaf factor of the articular leaf is calculated on eight different samles of that lant tye and the average leaf factor is calculated which is unique for a articular leaf tye and its value is stored in the database. When a new leaf is fed into the system for recognition, the leaf factor of that articular leaf is calculated and it is comared with the leaf factor which is stored in the database and the most matching leaf is returned as the outut. Image Acquisition Datasets are collected in such a way that leaves are catured against white background using a digital camera or through a scanner. For the roosed work, the dataset of 08 leaf images of 6 different secies are collected, which are in different Feature Extraction Image rocessing techniques are used to extract a set of features that characterize or reresent the image. The values of the extracted features reresent the information in the image. Arithmetic Mean: Also known as Averaging filter used to find the mean of the ixels in an image through moving an M* matrix over the ixels of the image and it finds the mean value of the ixels inside the mask, in the next ste it relaces the center ixel value with the mean value. Process is reeated till all the mean ixel value are calculated and assigned to the restored image. From this the individual intensity contribution of ixels is extracted. In the roosed methodology the use of arithmetic mean is to extract the colour feature from the inut Image. The result obtained after testing the mean value of different secies shows slight variation from secies to secies. Mean value of the images are given by the equation. A = n n x i () 544

4 International Journal of Alied Engineering Research ISS Volume, umber 7 (06) Research India Publications. htt:// In which, A is the average or arithmetic mean obtained, n is the number of terms (e.g. the number of items or numbers being averaged) and x i is the value of each individual item in the list of numbers being averaged. Standard deviation: It is one of the most used measure to find diversity in statistics. In Image rocessing it is ossible to find the variation by calculating the mean value obtained through standard deviation. Low in standard deviation indicates that data oints are very close to the mean and a higher deviation from the mean indicates that data oints sread in very large amount of area. This is calculated using the formula. S = Ai μ For a random variable vector A made u of scalar observations. Standard deviation on colour image and convex hull of the leaf are calculated. Convex Hull Ratio: Calculation of convex hull requires two satial arameters.first the area of the leaf and the second area of the convex hull on the leaf. The area of the leaf is calculated as the number of ixels in the foreground and the area of the convex hull is calculated as the number of ixels in the hull. The extraction of binary image and construction of convex hull from its binary image is shown in Fig.3. () Figure 4: Extraction of boundary 4πA Isoerimetric Quotient= (4) P Where A is the area and P is the erimeter of the leaf. The result can be used to redict the roundness of the leaf. Equation 4 is alied to get the isoerimetric quotient. Eccentricity: Eccentricity for an ellise with major axis Ma and minor axis Mi is defined by the equation 5. Ma + Mi Eccentricity= (5) Ma In order to extract the eccentricity of an object in a digital image, the best fitting ellise is first evaluated. The best fitting ellise is the ellise for which the sum of the squares of the distances to the given oints is minimal. In other words, the best fitting ellise is an ellise that best fits to the data oints contained in the region of interest. Fig. 5 illustrates a best fitting ellise to a olygon. Figure 3: Extraction of Convex Hull aleaf Convex Hull Ratio= (3) ahull Where a leaf is the area of the leaf and a hull is the area of the convex hull. Equation 3 is alied to get the convex hull ratio Isoerimetric quotient: Extraction of the isoerimetric quotient required extraction of the area and the erimeter of leaves which is shown in Fig. 4. Given a binary leaf image, the area is calculated as the number of ixels in the foreground. Lalacian filter is used to get the edge image for the foreground region and erimeter is calculated as the number of ixels in the boundary of the foreground region. Figure 5: Best Fitting Ellise to Polygon Eccentricity value is a scalar and it will also oints to the shae of the leaf. Entroy: Entroy is the statistical measure of randomness that can be used to characterize the texture of an Image. It hels in identify the similarity of two images. Here entroy is calculated on the gray scale image. Fig. 6 illustrates the gray scale conversion of the colour image. 545

5 International Journal of Alied Engineering Research ISS Volume, umber 7 (06) Research India Publications. htt:// Ste 7: Ste 8: Calculate Eccentricity (Ecc) & Isoerimetric Quotient (Iq) and Convex hull ratio (CHR) of BW Calculate Standard Deviation (Sc) of convex hull. Ste 9: Calculate Leaf Factor (Equation 7) Ste 0: Find out Minimum difference Leaf Factor from Database. IMPLEMETATIO & EXPERIMETAL RESULTS Figure 6: Gray Scale Conversion for Entroy Calculation i (log ) H= i (6) Where is the histogram count returned from imhist function in MATLAB. The result shows notable difference in different lant secies. lassification To classify a leaf according to their secies, the values that are extracted from re-rocessing stage are considered such as Mean, Standard Deviation, Convex hull ratio, Isoerimetric Quotient, Eccentricity and entroy. Let Pi be the various arameters extracted from the leaf image. Leaf factor of a leaf is calculated using the formula Pi μ + K factor [ P P ] (7) = P3 leaf + μ Where P denotes eccentricity and P denotes entroy and P3 denotes the standard deviation on colour Image. µ and K are given by equation 8 and equation 9. μ = (8) P i ( Pi μ ) K = (9) 4 σ Where µ is the mean of the arameters and K is the Kurtosis of arameters, where σ is given by equation 0. σ = i μ (0) Proosed Algorithm for Leaf Detection Ste : Read Image I Ste : Derive Standard Deviation (S I ) and Mean (A I ) of Ste 3: Ste 4: Ste 5: Ste 6: I Convert I to Gray scale gray Calculate entroy (En gray ) of gray Convert gray to binary BW Construct a convex hull (β) for BW Figure 7: Plant identification Stage Figure 8: Medicinal values of the Plant Figure 9: Detailed medicinal values of Secies. Leaf factor for different images of each secies is searately calculated and stored in the database. When a new image is given as inut, the system find the leaf factor for the leaf and find the most matching case from the database. The roosed algorithm yield 93.7% of accuracy which is calculated from Table and Visualized in Fig

6 International Journal of Alied Engineering Research ISS Volume, umber 7 (06) Research India Publications. htt:// umber of Secies Table : Leaf Detection Accuracy umber of Samles Correctly detected Samles Wrongly Detected Samles Figure 5: Test Result COCLUSIO The roosed methodology is tested with 08 different samle leaf images of 6 different secies and noticed ositive resonse in most cases. To identify a medicinal lant there is a need for exerienced taxonomists or a trained medicine ractitioner. Through this work, manual labor needed and time required to erform Ayurvedic secies recognition can be reduced. The roosed work can be extended to find the defected leaves to increase the accuracy. REFERECES Correctly detected Samles Wrongly Detected Samles [] Kadir, A., ugroho, L.E., Susanto, A. and Santosa, P.I., 03. Leaf classification using shae, color, and texture features. arxiv rerint arxiv: [] Ehsanirad, A. and Sharath Kumar, Y.H., 00. Leaf recognition for lant classification using GLCM and PCA methods. Oriental Journal of Comuter Science and Technology, 3(), [3] Perez, A.J., Loez, F., Benlloch, J.V. and Christensen, S., 000. Colour and shae analysis techniques for weed detection in cereal fields. Comuters and e lectronics i n agr iculture, 5(3),.97-. [4] Vijayashree, T. and Goal, A., 05. Classification of Tulsi Leaves Based on Texture Analysis. [5] Im, C., ishida, H. and Kunii, T.L., 998, ovember. A Hierarchical Method of Recognizing Plant Secies by Leaf Shaes. In MVA (. 58-6). [6] eto, J.C., Meyer, G.E., Jones, D.D. and Samal, A.K., 006. Plant secies identification using Ellitic Fourier leaf shae analysis. Comuters an d electronics in agriculture, 50(),.-34. [7] Coe, J.S., Remagnino, P., Barman, S. and Wilkin, P., 00. Plant texture classification using gabor cooccurrences. In Advances in V isual Com uting( ). Sringer Berlin Heidelberg. [8] Lee, K.B. and Hong, K.S., 03. An imlementation of leaf recognition system using leaf vein and shae. International J ournal of B io-science and B io- Technology, 5(), [9] Dhaygude, S.B. and Kumbhar,.P., 03. Agricultural lant leaf disease detection using image rocessing. International J ournal of A dvanced Research in El ectrical, El ectronics and Instrumentation Engineering, (), [0] Sannakki, S.S., Rajurohit, V.S. and Birje, S.J., Comarison of Different Leaf Edge Detection Algorithms Using Fuzzy Mathematical Morhology.International Journal o f I nnovations in Engineering and Technology (IJIET). [] Satti, V., Satya, A. and Sharma, S., 03. An automatic leaf recognition system for lant identification using machine vision technology. International J ournal of Engineering S cience a nd Technology, 5(4),.874. [] Kumar, S., 0. E, Leaf Color, Area and Edge features based aroach for Identification of Indian Medicinal Plants. International J ournal of Comuter Science and E ngineering, 3(3), [3] Du, J.X., Wang, X.F. and Zhang, G.J., 007. Leaf shae based lant secies recognition. Alied mathematics and comutation, 85(), AUTHOR PROFILE Pusha B R has comleted Master s degree in Comuter Alications at Visvesvarya Technological University, Belgaum, Karnataka and currently working as a Faculty in the Deartment of Comuter Science at Amrita Vishwa Vidyaeetham University, Mysuru Camus. Her areas of interests are Crytograhy and etwork Security. Anand C is currently ursuing Master s degree in Comuter Alications in Deartment of Comuter science at Amrita Vishwa Vidyaeetham University, Mysuru camus and comleted Bachelor s degree in Comuter Alications at Amrita Vishwa Vidyaeetham University, Mysuru camus. His areas of interest are Comuter vision and Pattern Recognition. Mithun ambiar P is currently ursuing Master s degree in Comuter Alications in Deartment of Comuter Science at Amrita Vishwa Vidyaeetham University, Mysuru camus and comleted Bachelor s degree in Comuter Alications at Adithya Kiran College of Alied Studies, Kannur University. His area of interest are Comuter Vision and Medical Image Processing. 547