Plant Leaves Recognition and Classification Model Based on Image Features and Neural Network

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1 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 11, Issue, No 1, March Plant Leaves Recognton and Classfcaton Model Based on Image Features and Neural Network Hong Fang 1, Hue L 1. Insttute of Basc Scence, ann Agrcultural Unversty, ann, Chna. Insttute of Computer and Informaton Engneerng, ann Agrcultural Unversty, ann, Chna Abstract In ths paper, on the bass of mage processng, plant leaves are respectvely extracted 7 HU nvarant moment egenvalues, three shape egenvalues and eght texture egenvalues based on gray level co-occurrence matrx. hen the paper adopts BP network, whch has been optmzed by L-M algorthm to dentfy the classes of the plant leaves based on 7, 10 and 18 egenvalues. he expermental results show that the classfcaton effect of 18 egenvalues s the best, the average recognton rate of whch s 100%, provdng a fast and effectve method for the dentfcaton of plant speces. Keywords: Image processng, Feature extracton, L-M algorthm, BP neural network, Classfcaton 1. Introducton Plant classfcaton s a bass and premse of plan research and development. Plant classfcaton and dentfcaton has a great sgnfcance and functon to clarfy the relatonshps and classfcaton system between speces, further to research on the orgn of speces, the dstrbuton center and the evoluton process and trend. Wth the contnuous development of the technology of the dgtal mage processng and pattern recognton, there are more and more studes on plant classfcaton and recognton based on the mage features of plant leaves. In ths paper, the researchers selected fve knds of common plant leaves as samples. On the bass of mage processng, we extracted the characterstc parameters of leaves and dentfed the selected leaves wth dfferent characterstc parameters usng the optmzed neural network, so as to get the effectve classfcaton model and method. sanderana), cleaned them, and got ther dgtal mages wth scanner. hen we leaded the dgtal mages nto a computer (completed n MALAB R010b envronment) and preprocessed the mages after readng the data.. Processng of plant leaf mages Frst of all, normalzed processng was conducted on the szes of the mages. hen the processed color mages were transferred nto gray-scale mages by weghted average method. It s the edge nformaton of the blade gray mages that s the key factor n the mage characterstc values extracton. herefore, ths paper used the medan flterng to take the nose for the mages. o dvde leaf and ts background nto bnary mages, the mages stll needed to do the threshold segmentaton to lay a good foundaton for the feature extracton. here are many methods of threshold segmentatons. In ths paper, the maxmum entropy threshold method was appled. Shown n Fg.1 and Fg.. Fg.1 Gray Image. Acquston and Processng of the Plant Leaf Images.1 Acquston of the plant leaf mages Frstly, we pcked fve knds of common plant leaves (they are respectvely euonymus aponcus, honeysuckle flower, populus tomentosa, chenopodum album and dracaena Fg. Bnary Image Copyrght (c) 014 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

2 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 11, Issue, No 1, March he Extracton of the Image Characterstc Parameters here are a lot of mage features for the classfcaton of plant leaves. hs paper selected the mage of HU moment nvarant features, shape features and texture features as the bass of the sample classfcaton. 3.1 he extracton of HU moment nvarant features HU nvarant moment method s a classc method of mage feature extracton. In 196, on the bass of algebrac nvarants, HU ntroduced the concept of mage moment nvarant, constructng seven moment nvarants of translaton, scalng and rotaton nvarance by usng the central moment. hen t was wdely used n mage recognton. Its prncple s as follows: suppose f ( x, y) s mage gray value on the spot ( xy,ts, ) ( p q) order center dstance s: p q ( x x ) ( y y ) f ( x, y) x y 0 0 (, 0,1,, ) (1) In ths formula: x0 m10 / m00, y0 m01 / m00 s a whole mage centrod coordnates. Wth the normalzaton of zero order central moments, we get the ( p q) order normalzed central dstance of mages: r / (n ths formula: r 1 ( p q) / )() 00 Usng the second order and thrd order normalzed central moments as well as the lnear combnaton, HU launched the followng seven moment nvarants group[1]: I I ( ) I ( 3 ) (3 ) I ( ) ( ) I ( 3 )( )[( ) 3( (3 )( )[3( ) ( I ( )[( ) ( ( )( ) I (3 )( )[( ) 3( (3 1 30)( 1 03)[3( 30 1 ) ( 1 03 (3) he groups of seven moment nvarants are mage translaton, rotaton and scalng nvarance, but because ts range s bgger, they may be negatve. herefore, ths paper adopts log I ( k 1,, 7) as characterstc k k parameters of nvarant moment, and the blade nvarant moment extracted through MALAB programmng s shown n table he extracton of shape features Accordng to the theory of plant classfcaton, the shape characterstc parameters of plant leaves are one of the most mportant and effectve bass for ts classfcaton, among whch the most useful one s the two-dmensonal shape parameter of the leaves. hrough comparng and analyzng the collected leaves, ths paper selects the three characterstc parameters ncludng the crcularty, rectangularty and elongaton [][3] wth good classfyng effect as the bass of classfcaton. Boundary track the bnary mage n the frst place to obtan the drecton nformaton of the mage boundary pxels and ther coordnate values. he boundary pxel nformaton s expressed n the pxel locaton and drecton of the chan code. Accordng to the eght drecton chan code calculaton, even number chan code number s N, odd number chan code number s N, permeter s L N N o, area A s expressed n the number of target n the whole mage pxels. Crcularty: C 4 A/ L (4) Rectangularty: R A/ A (5) w ( Aw s area of the mnmum crcumscrbed rectangle) Elongaton: E Rmn / R (6) max R, R s mnmum and maxmum dstance for the ( mn max center of mass of the border respectvely) he above three egenvalues of the leaves extracted by MALAB programmng are shown n table 1. able 1 shows that the absolute dfference of the dfferent leaves n three characterstc values s small, whch wll produce larger error for classfcaton and recognton of leaves. hus further consderate the texture characterstcs of the leaves as the samples of the classfcaton dentfcaton. 3.3 exture feature extracton based on gray level cooccurrence matrx exture feature s a basc attrbute of the obect surface, so dfferent plant leaves have dfferent texture characterstcs. hs paper adopts the method based on gray level co- o Copyrght (c) 014 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

3 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 11, Issue, No 1, March occurrence matrx to extract the texture characterstcs of the leaves. Gray level co-occurrence matrx reflects the comprehensve nformaton of mage gray scale dstrbuton n terms of change ampltude, drecton and local areas. Its defnton s the ont probablty dstrbuton wth two grayscale pxels n the mage wth the dstance d ( x, y) appearng at the same tme. In order to obtan the gray level co-occurrence matrx of the leaves, frst of all, gray the collected leaf mages. Because the gray scale of a grayscale mage s generally 56, t takes a long tme to calculate the spatal gray level cooccurrence matrx. herefore, under the premse that the texture feature s not affected, the orgnal gray scale of the mages s compressed to 16. In order to ncrease the overall mage contrast, before the compresson, the mages are through the process of hstogram equalzaton, whch ncreases the dynamc changes of the grayscale range. hrough the experment and comparson, the dstance between the pxel s 1, and calculate the gray level cooccurrence matrx n 0,45,90,135 four drectons. Comparng 14 characterstc values of grayscale cooccurrence matrx wth each other, ths paper selects the four characterstc parameters reflectng texture unformty, complexty, defnton and lnear correlaton, as follows[4]: Angular second moment: ASM [ p(, )] (7) (8) Entropy: EN p(, )log p(, ) Contrast: CON p(, )( ) (9) 1 Correlaton: ( ) p(, ) COR 1 (10) (Defnes: 1 p(, ) p(, ) 1 1 ( ) p(, ) ( ) p(, ) ) Extract the texture feature vector of leaf n the four drectons ( 0,45,90,135 ) by the normalzaton of gray level co-occurrence matrx. In order to get the texture feature nothng to drecton, ths paper take the mean and mean square error of each feature vector n four drectons as the texture characterstc parameters, whch s used as the bass of sample classfcaton. he eght texture feature values extracted by MALAB programmng are shown n table he BP Neural Network Classfcaton Model Based on L-M Optmzaton Algorthm 4.1 he structure and learnng rules of BP neural network BP network s one of the most wdely used neural network models. It s a knd of the multlayer feed forward network accordng to the error back propagaton algorthm tranng, consstng of postve nformaton dssemnaton and error back propagaton. he topology structure of BP network ncludes nput layer, hdden layer and output layer [5][6][7]. he structure s shown n Fg. 3. Input Hdden Output Fg.3 he structure of BP neural network. he basc dea of BP learnng algorthm s to solve the mnmum of error functon. It uses the steepest gradent descent method n nonlnear programmng. hrough the back propagaton of the network output error, BP learnng algorthm constantly adusts and modfes the network weghts and threshold to mnmze the network error, whose learnng process ncludes the forward calculaton and error back propagaton. Because the BP network has problems n practce such as low learnng effcency, slow convergence speed and easy to fall nto local mnmum pont, the L-M optmzaton algorthm s used to mprove BP neural network n ths paper. 4. Basc dea of L-M optmzaton algorthm L-M optmzaton algorthm s actually the combnaton of the gradent descent method and Newton s method. It has not only the local convergence of Newton s method, but also the global propertes of gradent descent method [8][9]. L-M optmzaton algorthm s manly calculated n the error square and mnmzaton. It adusts the weght Copyrght (c) 014 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

4 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 11, Issue, No 1, March threshold of Newton s method to 1 W ( J J I) J E In ths formula, means adaptve factor and E means matrx for the unt. When s very small, L-M optmzaton algorthm s close to the Newton algorthm; when tends to nfnty, t s the gradent descent method. It can be seen that L-M optmzaton algorthm can make the error correcton smooth between the two algorthms, so that the network has effectvely convergence, fnshng the teraton of the network n a shorter tme. 5. Experment Results and Analyss he characterstc parameters and classfcaton mode extracted by the above method are appled to the classfcaton and recognton of the fve knds of plant leaves, to test the classfcaton model. 5.1 he collecton of the tranng sample data able 1 lsts 18 characterstc parameters of a group of euonymus aponcas and populous tomentosa. In ths table, 1 ~, 7 C, R, E, 1 ~ respectvely ndcate egenvalues 8 of leaves, ncludng 7 HU nvarant moment values, crcularty, rectangularty, elongaton and the mean and mean square error of the leaf angular second moment, entropy, contrast, correlaton. able 1: he extracton result of the 18 leaf characterstc parameters Image C R euonymus aponcus populus tomentosa Image E 1 euonymus aponcus populus tomentosa Experments are dvded nto three ways: (1) selectng 7 HU moment nvarant feature values of the leaves as the tranng sample; () selectng10 egenvalues ncludng 7 HU nvarant moments nvarant feature values and the crcularty, rectangularty, and elongaton of the leaves as the tranng sample; (3) selectng 18 characterstc values (from able 1) as the tranng sample, n order to study the nfluence of dfferent leaf egenvalues on the classfcaton accuracy. 5. he structure of neural network hs neural network adopts the three-ter network structure ncludng the nput layer, hdden layer and output layer. And the nput layer respectvely ncludes 7, 10 and 18 neurons, correspondng to 7, 10 and 18 characterstc parameters of the leaves. Research has shown that for three layers of neural network, the number of hdden layer neurons s at least n/3 (n whch n s the number of neurons n the nput layer). So the number of hdden layer neurons s 0. here are 5 neurons n the output layer to dstngush 5 dfferent types of plant leaves. In 50 samples, we select randomly 70% are used for tranng, 15% for valdaton and 15% for forecast. 5.3 he results of the neural network tranng he number of nput neurons able : Comparng the classfcaton results of dfferent tranng sample datum Classfcaton accuracy R MSE e e e-005 he table shows that wth the ncrease of the selected plant leaf egenvalues, the correlaton of actual values and the predcted values become hgher and the classfcaton accuracy s gettng better. It nfers that, for the plant leaves, usng the HU nvarant moment s not a complete descrpton of the types of leaf features, because dfferent knds of plants have huge dfferent attrbutve characterstcs. At the same tme, the shape feature and texture feature have a great nfluence on the classfcaton. Copyrght (c) 014 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

5 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 11, Issue, No 1, March able 3: Leaf classfcaton confuson matrx (18 egenvalues) Leaf type Actual Value A B C D E Predcted value (%) A B C D E total (n able 3: A---euonymus aponcus, B---honeysuckle flower, C---populus tomentosa, D---chenopodum album, E---dracaena sanderana) able and able 3 show that takng 18 characterstc values as the classfcaton tranng sample of plant leaves can make classfcaton effect better and make the precson reach to 100%. By learnng 50 samples of neural network, we choose 10 samples from euonymus aponcus and dracaena sanderana whch have not partcpated the tranng to smulate the maturely traned neural network (18 neurons). he result s that the detecton accuracy of dracaena sanderana and euonymus aponcuss s 100% and 90.9% respectvely. here s one pece whch s recognzed as populus tomentosa. 6. Conclusons "Extracton and Recognton echnology Research of Leaf Image Feature".Computer Engneerng and Applcaton, No.3,006, pp [] ong Hou, Lgong Yao and JangmngGan, "Based on the Contour Features of Leaf Plant Identfcaton Research". Hunan Agrculture Engneerng, No.4, 009, pp [3] Le We, Dongan He and YonglangQao, "Based on Image Processng and SVM Plant Leaves Classfcaton Research". Agrcultural Mechanzaton Research, No.5,013, pp1-15. [4] Chengcheng Gao and XaoweHu, "Based on Gray Level Cooccurrence Matrx exture Feature Extracton". Computer System Applcaton,No.6, 010, pp [5] Shouku S and Xng Sun, Mathematcal Modelng Algorthm and Applcaton, Natonal Defence Industry Press, 01. [6] Samaneh Rastegar, M.Iqbal Bn Sarpan and Mohd Fadlee A.Rasd, "Detecton of Denal of Servce Attacks aganst Doman Name System Usng Neural Networks",IJCSI Internatonal Journal of Computer Scence Issues, Vol.6,No.1,009 ISSN(Onlne): [7] Attaruas Hcham, Bouhorma Mohammed and El Fallah Abdellah,"Sales Forecastng Based on ERP System through Delph, fuzzy Clusterng and Back-Propagaton Neural Networks wth adaptve learnng rate", IJCSI Internatonal Journal of Computer Scence Issues, Vol.6,No.1,009ISSN [8] Janme Wang and Wenzhong an, "Based on L-M Algorthm of BP Neural Network Classfer".Journal of Wuhan Unversty (natural scence edton),no.10, 005, pp [9] Bnme Lang, Xuelan Zeng, Melan Lang and Lnna We, "Based on L-M Algorthm Chna's Hgher Educaton Investment Scale Predcton Study".Journal of Guangx Unversty (natural scence edton), No.10, 009, pp Hong Fang, Assocate Professor of ann Agrcultural Unversty n the Mathematcs Department, the Drector of ann Industral and Appled Mathematcs Assocaton, manly engaged n the teachng of mathematcs and appled mathematcs research. Hue L,she s a student of ann Agrcultural Unversty. In ths paper, analyzng the characterstcs of plant leaves, the researchers present the extracton method of dfferent egenvalues based on the mages of leaves, and select the dfferent number of characterstc parameters of the same type of leaves to do the classfcaton by usng L-M algorthm, the optmzed BP neural network. he expermental results show that selectng 18 characterstc parameters of the leaves as the tranng sample data has the best classfcaton effect. hs paper llustrates that the characterstcs of the selected parameters and the neural network are feasble and practcal n the classfcaton of plant leaves. References [1] Xaofeng Wang, Deshuang Huang and Jxang Du, Copyrght (c) 014 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

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