A novel algorithm for damage recognition on pest-infested oilseed rape leaves

Computers and Electronics in Agriculture 89 (2012) 41–50

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Computers and Electronics in Agriculture

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A novel algorithm for damage recognition on pest-infested oilseed rape leaves

Yun Zhao a,b, Yong He a,⇑, Xing Xu c

a College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Chinab School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Chinac School of Mechanical and Automotive Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China

a r t i c l e i n f o a b s t r a c t

Article history:Received 20 January 2012Received in revised form 20 July 2012Accepted 26 July 2012

Keywords:Cabbage caterpillarOilseed rape leafHyperspectral imaging technologyDigital image analysisNeural networkInsect feeding injury

0168-1699/$ - see front matter � 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.compag.2012.07.014

⇑ Corresponding author. Tel.: +86 571 88982143.E-mail address: [email protected] (Y. He).

Cabbage caterpillar infestation of oilseed rape will leave wormholes on leaves. The percentage of worm-holes’ area on leaf is an effective index to evaluate infestation seriousness. Hyperspectral imaging tech-nology can be used to extract leaf from non-vegetation objects efficiently. Wormhole reconstruction canthen be carried out for counting the wormholes’ area. The reconstruction of wormholes that are entirelywithin the leaf contour can be easily processed by holes filling function. However, it is difficult to processwormholes at the edge of a leaf. A novel location factor and an improved genetic-wavelet neural networkreconstruction algorithm (G-WNNRA) have been proposed in this paper to process wormholes at the edgeof a leaf. For the edge of a damaged leaf, the infested part represented by a hole at the edge and non-infested part should be distinguished automatically. Thus the novel location factor which was basedon the first derivative of inverse function was used to develop test function for locating the infested part.Then the proposed G-WNNRA was constructed to reconstruct the missing part of an edge following thestep of learning the non-infested part of the edge. The topological structure and parameters of theG-WNNRA was optimized by genetic algorithm and morlet wavelet function was applied as a transferfunction. The points on non-infested part of edge were adopted as the training data set and the missingpart of the edge were predicted. During the prediction, the points making up the reconstructed edge werechosen based on the output of the G-WNNRA. For performance comparison, wavelet neural network(WNN), genetic neural network (GNN) and back propagation neural network (BPNN) were tested oninfested oilseed rape leaves and the RMSE of G-WNNRA was smaller than those of WNN, GNN and BPNN.The proposed location algorithm and G-WNNRA can be combined to reconstruct infested oilseed rapeleaves.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction imaging technology (Kim et al., 2000) and hyperspectral imaging

Oilseed rape is one of the most important oil crops worldwide.Numerous diseases and pests attack the plant as it grows, whichare difficult to control without chemical treatments (Alford et al.,2003). Insect resistance to the pesticides emerging due to thesuperfluous chemical treatments have increased the incidence ofpests (Valantin-Morison et al., 2007), thus one of the key issuesof pest management in conventional farming is to find an idealinfestation seriousness detection method which is helpful to con-trol the dosage of pesticide on certain condition. Visual detectionis a convenient method but is always subject to bias and inaccu-racy (Richardson et al., 2001; Steddom et al., 2005). For improvingthe accuracy of crop damage detection, some alternative methodshave been proposed, including various methods based on digitalimage analysis (Diaz-Lago et al., 2003), spectral technology (Guanand Nutter, 2002; Riedell and Blackmer, 1999), multi-spectral

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technology (Zhao et al., 2011; Reisig and Godfrey, 2010; Liuet al., 2010). These researches focused on alternative methods fordetecting the seriousness of crop diseases which were caused bymore than pest infestation. Among these researches, the methodsbased on spectral and hyperspectral imaging technologies haveled to more ideal performance to detect many kinds of crop dis-eases than others. And spectral and hyperspectral technologieshave also been used to analyze chemical composition for cropsand foods (Liu et al., 2008; Liu and He, 2008; Wu et al., 2007), thatmeans these technologies can be used to embody the features ofthe oilseed rape for further analysis.

Digital image analysis technology focuses on morphology andcolor feature of objects and can be used to recognize the edge ofpests in order to identify the pests and to obtain the number ofpests in pest infestation detection. Habib et al. (2000) and Habib(2000) worked on classification of cotton pests where pests, leavesand other foreign objects in cotton field were recognized by anadaptive neuro-fuzzy control system based on digital image analy-sis technology. The research concluded that the digital image anal-ysis is very useful to quantify the damages caused by pests. In

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42 Y. Zhao et al. / Computers and Electronics in Agriculture 89 (2012) 41–50

addition to digital image analysis, spectral, multispectral andhyperspectral technologies can also be used to quantify pest dam-ages. Spectral technology has been utilized to detect crop damageseverity caused by pests. Some studies have researched reflectancespectra features of wheat leaves infestation by aphids (Mirik et al.,2007, 2006a). Genc et al. (2008) has estimated the vegetationinduces of wheat field infested by sunn pests from reflectance spec-tral data to detect the injury severity. Other researchers have uti-lized spectral data sets to detect minor damages on tomato leaves(Xu et al., 2007), thrip damages on sugarcanes (Abdel-Rahmanet al., 2010) and aphid and spider damages on cottons (Reisig andGodfrey, 2007). Yang et al. (2005) and Yang (2005) used multispec-tral data to detect greenbug infestation on wheat field. Each of thespectral, multispectral and hyperspectral technology collects datacovering certain range of wavelength and the key difference amongthem is the spectral resolution. Hyperspectral technology coverslarger range of wavelength with smaller intervals of wavelengthsthan spectral and multispectral technology. Besides reflectancedata collection, hyperspectral imaging technology also measuresthe gray image on each wavelength. Thus hyperspectral imagingtechnology can sense more information of objects. Some studieshave focused on the pest infestation detection based on hyperspec-tral imaging technology and concluded that the hyperspectral datalead to better detection accuracies. Mirik et al. (2005, 2006b) differ-entiated wheat infested or uninfested by aphididae, and estimatedaphididae damages on wheat by analyzing the hyperspectral reflec-tance and image data. But the hyperspectral imaging technology forcrop pest damage detection is still not very widely applied.Airborne remote sensing is a kind of application of spectral andhyperspectral technology. It is suitable for large scale measurebut expensive and the accuracy may be influenced by weathercondition or air quality. Elliott et al. (2007, 2009) detected aphidinfestation on wheat field by measuring and analyzing airborneremote sensing data. Yang et al. (2009) differentiated wheatstressed by greenbugs and Russian wheat aphids based on remotesensing technology. Hyperspectral imaging is accomplished by spe-cific hyperspectral camera and normally leads to better perfor-mance on pest infestation detection when combined with digitalimage analysis. Mirik et al. (2006c)’s research concluded that spec-tral reflectance data combined with digital image analysis reachedan ideal performance of greenbug damage quantification. This con-clusion is a good reason to introduce hyperspectral imaging tech-nology during the process of quantifying pest damages on crops.

Neural network, as one of the most important computationalintelligence method that is inspired from biological neural net-works, has been used in many researches to analyze features ofcrops and products, including damage severity classification andregression algorithm. Neural network was built to count and clas-sify dark specks of tomato paste (Velioglu et al., 2011). Arribas et al.(2011) established an automatic sunflower leaf image classificationsystem based on neural network. Neural network has been used todevelop a classifier with potential for rapid field separation of cropand weed species (Tyystjarvi et al., 2011; Jeon et al., 2011). Neuralnetwork modeling method was also trained to assess insect growth(Patten et al., 2011). All these researches concluded that neuralnetwork is an ideal modeling method resulting in high accuracyin agriculture pattern recognition.

Research about pest infestation detection on oilseed rape basedon technology which combined hyperspectral imaging, digitalimage analysis and computational intelligence has not beenreported yet. Cabbage caterpillar is a serious pest of oilseed rapewhich always bites mesophyll and leaves wormholes on leaf. Thereis not yet an existing uniform criterion to judge the damage extentcaused by cabbage caterpillars. Spectral reflectance values of fea-ture wavelengths which can be sensed by hyperspectral cameraare useful to distinguish the leaf and background. It will be more

convenient for background segmentation and for leaf extraction.Therefore, the hyperspectral imaging combined with digital imageanalysis can be used to extract leaves from non-vegetation objectsmore efficient than conventional digital image analysis, and it isthe basics of the study. And then, digital image analysis and com-putational intelligence can be combined to extract the edge ofleaves. Different neural network models can be used to reconstructthe missing part of an edge based on the data extracted fromhyperspectral imaging and digital image analysis steps. And theseprocessing will be useful to estimate the percent of damaged areaon leaves and detect damage extent.

The goal of the study was to detect the wormhole on oilseedrape leaf based on hyperspectral imaging technology, digital imageanalysis and neural network, which included five stages: (1) back-ground segmentation based on hyperspectral imaging technology,(2) closed wormhole detection, (3) edge extraction, (4) unclosedwormhole location and (5) unclosed wormhole reconstruction. Anovel unclosed wormhole location algorithm and reconstructionalgorithm were proposed primarily in the paper. The definitionsof closed and unclosed wormhole would be described in section‘Materials and methods’. The study will be useful to the next re-search stage of oilseed rape damage severity estimation bycabbage caterpillars.

2. Materials and methods

2.1. Sample preparation and hyperspectral image processing

The oilseed rape breed ‘Zhejiang Province double 758, China’was used in the experiment and infected by cabbage caterpillarsnaturally in May and June, 2011 in Zhejiang Province, China. Thirtyinfected leaves at budding stage were chosen and kept on plantsduring sampling in order to enhance the applicability of the meth-od proposed in this paper.

Hyperspectral image cubes can be used to distinguish variousobjects efficiently because of the extensive wavelengths many ofwhich cannot be sensed by human eyes. Thus hyperspectral imag-ing technology was applied to sample leaves with the purpose ofcollecting enough information. The hyperspectral camera scannedthe leaves from above and 510 gray-scale images correspond to510 wavelengths at the range of 380–1030 nm were collected.The size of the hyperspectral image cube was 672 � 409 � 512data points. In order to collect the relative reflectance informationof the leaves, the hyperspectral image cubes were corrected bystandard white image and dark image. The gray-scale values tocompose the images are the reflectance spectral data on corre-sponding wavelength. In the 510 wavelengths, 640 nm, 550 nmand 460 nm can be sensed by human eyes which are normally usedto compose the true color image just similar to what human eyescan see. In order to present the visual field of camera, a true colorimage of the damaged oilseed rape leaf is shown in Fig. 1A. How-ever, it has hardly anything to do with further analysis which justdepends on the reflectance spectral data on the three wavelengths.The reflectance spectral data corresponding to the wavelengthswhich cannot be sensed by human eyes is useful and that is whythe hyperspectral technology was adopted in the study.

There are various objects such as soil, worm and other back-ground in the field of vision of hyperspectral camera. Featurewavelength of these objects should be selected to present the dif-ference among them and in certain situation band difference calcu-lation can be used to enhance the disparity of the wavelengths ofvarious objects. Therefore, background segmentation was accom-plished by the band difference calculation performed on hyper-spectral feature wavelengths.

500 600 700 800 900 10000

500

1000

1500

2000

2500

3000

Smoo

thed

Y1

wavelengths

leaf worm background

A

B

Fig. 1. (A) The true color image of a damaged oilseed rape leaf. (B) The reflectancespectra of the leaf, including three objects: leaf, background and worm. (Forinterpretation of the references to color in this figure legend, the reader is referredto the web version of this article.)

Y. Zhao et al. / Computers and Electronics in Agriculture 89 (2012) 41–50 43

One pixel of each kind of objects, such as the leaf, worms andbackground on the image were chosen and the reflectance spectraof them are shown in Fig. 1B, all of them covering the range of 380–1030 nm. The spectra of worms and the leaf are similar but those ofbackground are very different. Essentially, worms always stay onthe leaf which means the area covered by worms can just be con-sidered as the area of the leaf. Thus the segmentation will be per-formed simply on one class of pixel of worms and the leaf andanother one of background. The spectra in Fig. 1B illustrate thatthe band difference calculation performed on wavelength of784.55 nm and 682.27 nm will enhance the disparity betweenthese two classes of pixels. The band difference calculation wasperformed as

R ¼ R784:55 � R682:27 ð1Þ

where R is the band difference calculation result, R784.55 and R682.27

are the reflectance spectral data on wavelengths of 784.55 nm and682.27 nm, respectively.

Each result value of the band difference calculation correspondsto one pixel on the image which means the values can compose agray-scale image. The difference between the result values of back-ground and the class of leaf and worm were larger than originalspectral data. Noise reduction and binary conversion was appliedon the gray-scale image then. During the binary conversion, thethreshold of 1000 was suitable and the pixels with the result val-ues smaller than 1000 were set as 0 and the others were set as 1.For the sake of analysis, region of interest (ROI) tool was used toeliminate the incomplete leaves on the bottom of the image. Theresults are shown in Fig. 2. The further analysis will be performedbased on the results.

2.2. Types of wormholes

After background segmentation, the wormholes were classifiedinto two types: closed wormholes which were entirely within theleaf contour and unclosed wormholes which were at the edge of aleaf. Classification of wormholes was important to categorize themethods for restoration. For closed wormholes, hole filling func-tion can be used to restore and the number of the pixels of therestored part can be calculated. For unclosed wormholes on whichthe study is focused, a novel algorithm of wormhole location andreconstruction was proposed in the study.

2.3. Algorithms of unclosed wormholes location

After the filling function was performed during closed worm-holes processing, we defined that the closed edge of a leaf with un-closed wormholes was the damaged edge, the closed edge of a leafwithout unclosed wormholes was the original edge, the part of thedamaged edge which coincided with the edge of unclosed worm-hole was the bitten edge, and the part of the edge which coincidedwith the original edge was the unbitten edge. After edge extraction,the damaged or original edge was composed of discrete pixels.Curve fitting was necessary for the next analysis. The first step ofunclosed wormholes location was to sort the discrete points onthe edge. The points should not be sorted according to x or y valuesbecause in that case, the points cannot be sorted along the edgecurve and then there were vertical or horizontal lines stretch overthe leaf area when it was fitted. The best method to make thepoints sorted consecutively is to map them onto polar coordinatesystem and to sort them according to the polar angles from 0� to360�. Sometimes, there are more than one points on one polar axis,which will cause problem for curve fitting, thus only one point canbe chosen on one polar axis. The point with the largest polar radiuswas chosen to avoid the leaf area reducing.

Then the chosen and sorted points on the edge were trans-formed onto Cartesian coordinate system for curve fitting. Fittingfor a closed curve is not very easy because there are more thanone points corresponding to one coordinate axis. So the edge curvecan be decomposed into two components according to x and yaxises and the parametric equation of the curve can be establishedas

x ¼Xm

n¼0

anhm�n

y ¼Xm

n¼0

bnhm�n

8>>>><>>>>:

ð2Þ

where h is the parameter, from 0 to 6.28 in radians, an and bn are thecoefficients, m is a natural number larger than n.

The bitten and unbitten edges on a damaged edge should bedistinguished automatically so that the reconstruction processcan be focused on the bitten edges solely. It will be better to detectthe bitten edge according to the polar angle because in furthersteps the reconstruction can be performed on certain range of polarangles. For this reason, a location factor based on the first deriva-tive of inverse function of the parametric equation was proposedto detect the bitten edge. The location factor was calculated as

D ¼dxdh

��h

dydh

��h

0B@

1CA ð3Þ

where D is the location factor, h is the polar angle of each point ofedge.

A test function was established in which h was the independentvariable and D was the dependent variable. When the test function

Fig. 2. The results of band difference calculation and the binary conversion, showing the grayscale image of band difference calculation (left panel), the binary image (middlepanel), and the image without incomplete leaves (right panel).


of an original edge mapped onto Cartesian coordinate, the functioncurve always included two pulses at around h equals 1.57 radian and4.71 radian because the slope of the edge curve changed from posi-tive to negative at around that polar angle. So these two pulses wereconsidered as marker pulses. But for the test function of a damagededge, the marker pulses coexisted with other abnormal pulsescalled wormhole pulses which were caused by discontinuouschanging of the slope of the edge curve. Therefore, the step of locat-ing the unclosed wormhole was equivalent to search for the worm-hole pulses on the test function. Because an original edge is zigzagnaturally, the small pulses on the test function should not be consid-ered as wormhole pulses. A threshold of pulse amplitude is impor-tant to distinguish normal zigzag edge and unclosed wormhole. Ifthe threshold is too large, pulses caused by unclosed wormholes willbe missed. On the other hand, if the threshold is too small, the num-ber of abnormal pulses will increase although the true abnormalpulses are rarely missed. Through times of tests and analyses, athreshold of 5000 was suitable for detecting the abnormal pulsesefficiently. Thus on the test function, for a damaged edge, thereare abnormal pulses larger than 5000 at the range of bitten edge;for an original edge, there is no abnormal pulse on the test function.

The procedure includes:

Step 1: sort the discrete points of edge.Step 2: fit edge curve.Step 3: calculate the location factor and develop the testfunction.Step 4: determinate bitten edge through abnormal pulsesdetection.

2.4. Algorithm of unclosed wormholes reconstruction

2.4.1. Data setFor bitten edge reconstruction, the constraint point set was

needed to train the neural network model. The constraint pointsincluded the discrete points of edge which were called edge points,the discrete points outside the closed edge which are called exter-nal points, and the discrete points inside the closed edge whichwere called interior points. Discrete points of edge can be repre-sented as (h,c) where h was the polar angle and c was the polar ra-dius. For any constraint point f(h,c), the constraint value g(h,c) isassigned by

gðh; cÞ ¼0; ðh; cÞ 2 E

1; ðh; cÞ 2 O

�1; ðh; cÞ 2 I

8><>: ð4Þ

where E is the space of edge points, O is the space of external pointsand I is the space of interior points. The zero points should be in-cluded to establish calibration data set. In addition, some external

points and interior points should also be included in order toapproximate the reconstructed edge. The input vector of neural net-work model was two-dimension which was composed of h and c.The constraint values composed the output vector of neuralnetwork model.

2.4.2. Genetic-wavelet neural network reconstruction algorithm(G-WNNRA)

The reconstruction was performed by neural network with two-dimension input vectors of h and c and one-dimension output vec-tors of constraint values g(h,c). All points on the same polar axiswith different polar radius are candidates but only one is potentialpoint of reconstructed edge. The one with the constraint valueclosest to zero should be considered as the point of the recon-structed edge.

By introducing wavelets into neural networks, wavelet neuralnetworks have been developed. Wavelet neural networks canconverge quickly and give high precision because of the time–frequency localization properties of wavelets (Abiyev and Kaynak,2008). The wavelet neural network can be divided into two catego-ries: loose and tight. In the loose type, the wavelet transform isused to preprocess the input signal of the network. The input sig-nals are firstly decomposed as multi-scale coefficients by wavelettransform and then the coefficients are inputted into neural net-work. The drawback is that for multi-scale coefficients, there aremulti neural network needed to be developed correspondingly.Thus the computational complexity is increased. Yaghobi et al.(2011) structured Meyer wavelet probabilistic neural networkwhich is a kind of loose network to detect internal faults in sali-ent-pole synchronous generator. Wavelet transform also can beused as transfer functions because of the good modeling propertiesover a range of frequencies. In the tight type, the wavelet functionsis used as transfer functions instead of local functions such asGaussian and sigmoid functions in the hidden layer of the neuralnetwork. The neural network was constructed based on the tighttype in this study.

Neural network with the topological structure which is setempirically may increase computation and decrease stability ofprediction performance especially when there are different inputvectors. Dhakal et al. (2011) adopted neural network to predictthe freshness of eggs based on Haugh Unit. It resulted in satisfiedprediction accuracy, but the topological structure of the networkwas set manually without optimization and the structure with11 hidden layers caused large computation. The optimizing ofthe topological structure and parameters can generate an efficientnetwork. The optimization was performed by Genetic algorithm inthis study. The topological structure was decoded as binary codewhich included the number of hidden layers all over the networkand the number of neurons on each layer. Parameters weredecoded as real code which included shape parameter of transfer


function, weights between two neurons, thresholds of notes onhidden layers and output layers for the next steps of optimization.In Genetic algorithm, the fitness function was applied to evaluateeach individual of the current solution. The selection, crossoverand mutation operators were adopted repetitively to create newindividuals of the code of topological structure and parameters ofnetwork until the last individual passed the fitness criteria and bet-ter new network would be created.

As mentioned above, an improved neural network model com-bined with Genetic algorithm and wavelet transform was proposedfor leaf reconstruction, called G-WNNRA. The transfer function ofthe neural network was set as morlet wavelet function, and the ori-ginal and derivative function of morlet wavelet function will beused in the network. The morlet function is shown below wherex is the input of function:

f ðxÞ ¼ exp � x2

2

� �cosð1:75xÞ ð5Þ

The derivative function is:

f 0ðxÞ ¼ �ðaxÞ � 1:75 exp �n2

2

��sinð1:75xÞ ð6Þ

And the Genetic algorithm was adopted to optimize topologi-cal structure of network and weights and thresholds of neurons.Morlet wavelet function was adopted as the transfer function, thusthe shape parameters do not need to be optimized.

For edge reconstruction, the points with the same polar anglesas the points on unbitten edges played the role as the inputs ofthe G-WNNRA to establish the calibration model and the outputswere the constraint values of these points. The calibration dataset should include three kinds of points with constraint values of�1, 0 and 1. More than one point with constraint values of 1 and�1 on each polar axis should be inputted into the neural networkin order to choose the right one. And then the points with the samepolar angles as the points on bitten edges consist the test data setin order to predict the points of reconstructed edge. For reducingcalculation time, the points consist the test data set should be lim-ited according to the polar radiuses. The points with the values ofpolar radiuses smaller than the one with largest polar radiuses onunbitten edge and greater than the one with smallest polar radi-uses can be chosen. Then the point on each polar axis with the pre-dicted constraint evaluation closest to zero was chosen as a pointof reconstructed edge.

The procedure of G-WNNRA:

Step 1: Set the transfer function as Morlet wavelet function.Step 2: Input the calibration data set into initial network.Step 3: Optimize the topological structure by geneticalgorithm

1. decode the structure by binary code;2. maximum number of generations = 60;3. generation gap = 0.9;4. calculate fitnesses;5. Monte Carlo selection;6. one-point crossover;7. Gaussian mutation;

Step 4: Optimize the parameters of the network by geneticalgorithm

1. decode the structure by real code;2. maximum number of generations = 100;3. generation gap = 0.9;4. calculate fitnesses;5. Monte Carlo selection;6. two-point crossover;7. Gaussian mutation;

Step 5: Edge reconstruction
1. input the test data set into the improved neural
network;2. reconstruct the edge.

The block diagram of the whole leaf reconstruction system isshown in Fig. 3.

3. Results and discussion

3.1. The closed wormholes

The closed wormholes are the damaged areas entirely withinthe leaf contour. Based on the result in Fig. 2, in order to processclosed wormholes on the leaf, a holes filling function was applied.The filled leaf is shown in Fig. 4. The number of pixels of worm-holes was calculated as Eq. (7) and the percentage of the closedwormholes’ area on the leaf was calculated as Eq. (8).

Nc ¼ Nfilled � Nunfilled ð7Þ

Pc ¼Nc

Nfilledð8Þ

where Nfilled is the number of pixels of the filled leaf shown in Fig. 4and Nunfilled is the number of pixels of the leaf on the right image ofFig. 2. Nc is the number of pixels of the wormholes.

3.2. Edge extraction

After closed wormholes processing, all of the holes within theleaf were filled. For further processing, the edge of the filled leafshould be extracted. The edge was the damaged edge which hadbeen defined in the section of ‘‘Algorithms of unclosed wormholeslocation’’. The Sobel operator, a kind of discrete differential opera-tor suitable for edge extraction especially, was adopted to extractthe damaged edge. The threshold of Sobel operator was deter-mined as 12.465 by OTSU-maximum variance method, whichwas a kind of adaptive threshold method and proposed by Otsufrom Japan in 1979. The extraction was performed along boththe vertical and horizontal directions. And for further analysis,the edge was mapped onto polar coordinate system so that the dis-crete points on the edge could be sorted by the polar angle. Theedge of the leaf is shown in Fig. 5.

3.3. The unclosed wormholes

3.3.1. Bitten edge locatingThe unclosed wormholes are the holes at the edge of a leaf. In or-

der to evaluate the extent of damage, the holes at the edge shouldbe detected. In other words, the bitten edge should be detected.The damaged edge was mapped onto polar coordinate system(shown in Fig. 5) for sorting the discrete points of the edge basedon polar angle. From 0� to 360�, one point was extracted once thepolar axis rotated one degree. If there were more than one pointon the same polar axis, the point with the largest polar radiuswas extracted. Then, the sorted discrete points were transformedonto Cartesian coordinate system for curve fitting. It will be betterto fit the closed curve according to x and y directions respectively.For the damaged edge curve mapped onto Cartesian coordinate sys-tem, Fig. 6A shows the fitting curves in which the black (solid) curveis for the x direction and the red (dashed) one is for the y direction.The parametric equation of the edge curve is shown in Eq. (9) andthe coefficients of Eq. (9) is shown in Table 1.

Fig. 3. The block diagram of the whole system proposed in the study.

Fig. 4. The filled leaf. The closed wormholes on the leaf have been filled by applyinga holes filling function.


x ¼X30

n¼0

anh30�n

y ¼X30

n¼0

bnh30�n

8>>>><>>>>:

ð9Þ

The location factor D which was the first derivative of inversefunction of Eq. (9) was calculated and a test function was devel-oped. The test function was mapped onto a coordinate with thehorizontal axis representing radian and the longitudinal axis repre-senting location factor D which was shown in Fig. 6B. For the testfunction of an original edge, there were always two pulses appearat around 1.57 radian and 4.71 radian called marker pulses and noother pulses larger than 5000 appear on other radians. For a dam-aged edge, there was a wormhole pulse corresponding to each bit-ten edge. And the region of the radian of wormhole pulse plus andminus 0.5 was regarded as the radian of the bitten edge. Thus, forthe damaged edge, the wormhole pulses appeared at 3.7 radianand 5.2 radian and the corresponding bitten edges ranged from3.2 to 4.2 radian and from 4.7 to 5.7 radian. The result is shownin Fig. 6B.

3.3.2. Damaged edge reconstructionFor damaged edge reconstruction, the calibration and predic-

tion data sets for the G-WNNRA network need to be established.

0 1 2 3 4 5 6

-600

-400

-200

0

200

400

600

800

X, Y

x component y component

Radian

0 1 2 3 4 5 6

-20000

-15000

-10000

-5000

0

5000

10000

Loca

tion

fact

or D

Radian

marker pulse

damaged marker pulse

A

B

Fig. 6. Fitting curves and test curve of the leaf edge. (A) Fitting curves on x and ydirection respectively. The leaf edge curve in Fig. 5 was decomposed into twocomponents according to horizontal and vertical directions. The horizontal axis isthe radian and the vertical axis is the decomposed value. The black (solid) curve isthe x component and the red (dashed) curve is the y component of the leaf edgecurve. (B) The test curve which is the first derivative of inverse function of fittingcurve. The horizontal axis is the radian and vertical axis is the value of locationfactor which is the first derivative of inverse function of fitting curve. The pulsesmarked with red (dashed) ellipses are marker pulses. Other pulses marked withblue (dot) ellipses are damage marker pulses. The pulses appear at 3.7 radian and5.2 radian are wormhole markers and damaged ranges on the edge of the leaf are3.2–4.2 radian and 4.7–5.7 radian. (For interpretation of the references to color inthis figure legend, the reader is referred to the web version of this article.)

Fig. 5. The edge of the leaf which is mapped onto polar coordinate. The edge of theleaf was extracted and mapped onto polar coordinate system for further analysis.


The points on unbitten edge of a leaf were part of the calibrationdata set and the constraint values of them were 0. And the dataset also included the discrete points whose polar angles were equalto the polar angles of the points on unbitten edge. The polar radi-uses of the discrete points were equal to the values of polar radi-uses of points on unbitten edge plus 1 and the values minus 1.Thus the constraint values of them were 1 and �1. Therefore, thetwo-dimension input vectors used to optimize the network werecomposed of polar radiuses and polar angles and the one-dimen-sion output vectors were the constraint values. Then the Morletwavelet function was set as the transfer function and the geneticalgorithm was utilized to optimize the topological structure ofthe G-WNNRA network. The optimized topological structure ofthe G-WNNRA developed in the study was 3-layer with 4 neuronson the hidden layer and the parameters of the network are shownin Table 2.

Then the prediction data set were developed including the dis-crete points of which the polar angles were the same as the pointsof bitten edges and the polar radiuses should be limited in therange of the largest and smallest values of radius of points of unbit-ten edges in order to reduce the retrieve scope. Thus the polar radi-uses of the points were limited at the range of 250–620 in thestudy. The polar radiuses and angles of these points were inputinto the G-WNNRA and the outputs were the predicted values ofconstraint.

There is only one point on each polar axis that can be consid-ered as a point of reconstructed edge. On each polar axis, the pointwith the output constraint value closest to zero was chosen as thepoint of reconstructed edge. The reconstruction result is shown inFig. 7. The closed black curve is the damaged edge and the redcurves are the reconstructed edge. The red curves were predictedby the G-WNNRA model developed on the basis of the unbittenedges which were part of the closed black curve. When the recon-structed edge and damaged edge coexist on a same coordinate,there might be a special phenomenon that the radiuses of thepoints on the reconstructed edge are smaller than the points onthe bitten edge. In that case, the edge of which the points with lar-ger radiuses should be considered as the constructed edge. Becausethe contour of the damaged leaf is complicated, the reconstructionresult shown in Fig. 7 can be considered as an ideal result.

Because the original edge of a leaf had been damaged by thepests, the successfulness of estimating the performance of recon-struction depends on whether the expected reconstructed edgeconnects with two ends of unbitten edge, and smooth, of coursethe normal zigzag which always appears at the contour of undam-aged leaf ought to be ignored. In order to estimate the predictionperformance accurately, undamaged leaves were collected andthen were damaged artificially. The original edges and damagededges were extracted and the damaged edges were located and

reconstructed. Then the performance of the method proposed inthe study was estimated accurately. The procedure will be de-scribed in next section.

3.3.3. Reconstruction performance comparisonIn order to evaluate the performance of the proposed G-WNNRA

model, four mathematical prediction models were carried out foredge reconstruction: genetic neural network (GNN), wavelet neu-ral network (WNN), back propagation neural network (BPNN)and G-WNNRA. Thirty undamaged oilseed rape leaves wereadopted and their original edges extracted. Then the leaves weredamaged artificially to simulate unclosed wormholes and the dam-aged edges were extracted. Thus there were 30 damaged edges and30 corresponding original edges. The unclosed wormhole locationmethod proposed in the study was performed on the 30 damagededges resulting in distinguishing bitten and unbitten edge for eachdamaged edge. The bitten and unbitten edges of a same leaf wereused to evaluate the performance of the unclosed wormhole recon-struction method proposed in the study. Certain points of each leafwere used to establish one prediction model. The points with the

Table 1Coefficients of fitting parametric equation of the leaf edge curve.

an a0 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10

0.0000 0.0000 0.0007 0.0004 0.0067 0.0041 0.0404 0.0239 �0.1634 0.0921 0.4624a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 a21

0.2469 �0.9405 0.4716 1.3876 0.6464 1.4804 0.6309 1.1250 0.4272 �0.5922 �0.1900a22 a23 a24 a25 a26 a27 a28 a29 a30

0.2063 0.0495 �0.0440 �0.0056 0.0048 0.0000 0.0000 �0.0000 �0.0001

bn b0 b1 b2 b3 b4 b5 b6 b7 b8 b9 b10

�0.0000 �0.0000 0.0009 0.0006 �0.0094 �0.0056 0.0558 0.0333 �0.2192 �0.1300 0.5980b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21

0.3506 �1.1607 �0.6684 1.6159 0.9050 �1.6064 �0.8613 1.1215 0.5603 �0.5328 �0.2362b22 b23 b24 b25 b26 b27 b28 b29 b30

0.0003 0.1630 0.0583 �0.0289 �0.0067 0.0023 �0.0001 �0.0003 0.0000

The coefficients would be substituted into Eq. (9) to form a parametric equation in order to fit the curve of the leaf edge.

Table 2Optimized parameters of G-WNNRA.

Weights ThresholdsInput to hidden layer Hidden to output layer Hidden layer Output layer

1st Neuron 2nd Neuron

�9.9185 5.9427 10.2632 �11.9431 3.4742�6.4537 12.5619 1.8475 �11.0937�7.5622 2.9282 3.5571 �19.2823�13.2413 15.0208 �0.9563 �11.6925

The G-WNNRA model contains three layers, two neurons in input layer, four neurons in hidden layer and one neuron in output layer. The values in the first column are theweights from first neuron in input layer to four neurons in hidden layer respectively. The values in the second column are the weights from second neuron in input layer tofour neurons in hidden layer respectively. The values in the third column are the weights from four neurons in hidden layer to output layer. The values in the fourth and fifthcolumns are the thresholds of neurons in hidden layer and output layer. Based on these weights and thresholds, a real G-WNNRA model is established.

Fig. 7. Reconstruction result. The reconstruction was performed by G-WNNRAmodel developed on the basis of the unbitten edges which were part of the closedblack curve. The closed black curve is the damaged edge and the red curves are thereconstructed edge. The contour of the damaged leaf is complicated, thus thereconstruction result is acceptable. (For interpretation of the references to color inthis figure legend, the reader is referred to the web version of this article.)


same polar angles as the points of unbitten edge were used to de-velop the calibration model, and the calibration data set includedthe polar radiuses and polar angles of the points as inputs andthe constraint values of the points as outputs, validation data set.The validation model was developed in order to predict the zeropoints (the points with the constraint value of zero) which wereexpected to be the points of the predicted edge. And the constraintvalues of the points of the original edge played the role as referenceoutputs, always zero in fact, used to contrast with the predictedoutputs to evaluate the performance of the method. The GNN,WNN, BPNN and G-WNNRA which were four kinds of regressionmodels were utilized on each leaf to establish the calibration andvalidation models, respectively.

In the GNN model, genetic algorithm was applied to optimizethe topological structure and parameters, and the transfer functionwas set as Tansig transform function. The generation number andgeneration gap were set manually as 100 and 0.9 respectivelywhich were the same as G-WNNRA in order to keep comparabilitywith G-WNNRA. During the WNN modeling, the topological struc-ture was set manually as one hidden layer with six neurons and thetransfer function was set as Morlet wavelet function. Parameters ofthe network were obtained by training the data sets of points inthe form of feed-forward propagation and errors feed-back propa-gation. The WNN model was developed after 100 times of iterativelearning. BPNN is a kind of conventional neural network learningalgorithm and it was developed in the study. The superiorities ofG-WNNRA is that the transfer function of it was set as Morletwavelet function but the common Tansig transfer function wasused in GNN and the topological structure of G-WNNRA was opti-mized by genetic algorithm but it would be set manually in WNNdevelopment, inversely.

Prediction performances of the four mathematical reconstruc-tion models are shown in Table 3. The prediction performance ofcalibration model developed by calibration data set was calculatedbased on the reference constraint values and the predicted

constraint values of the points of unbitten edges of 30 leaves. Theprediction performance of validation model developed by valida-tion data set was calculated based on the reference constraint valuesand the predicted constraint values of the points of bitten edges of30 leaves. According to Table 3, the performance of G-WNNRA

Table 3Performance comparison among different models.

Correlation (R) Determination (R2) RMSE Bias Slope Offset

Calibration model, points of unbitten edge GNN 0.982 0.964 0.0228 0.024 0.975 0.01WNN 0.976 0.953 0.0342 0.008 0.947 0.017BPNN 0.947 0.897 0.0375 0.023 0.912 0.029G-WNNRA 0.998 0.996 0.00681 0.009 0.99 0.001

Validation model, points of bitten edge GNN 0.915 0.837 0.0312 0.026 0.887 0.045WNN 0.887 0.786 0.0529 0.059 0.876 0.043BPNN 0.869 0.755 0.0603 0.073 0.801 0.072G-WNNRA 0.953 0.908 0.02714 0.047 0.964 0.028

Calibration model and validation model were established with the polar radius and polar angle of the discrete points of edge of leaf as inputs and the constraint value asoutput. The calibration data set was developed based on the points with the same polar angles as the points of unbitten edge and validation data set based on the pointswhich have the same polar angles as the points of bitten edge. R2 is the fitting degree of the predicted constraint values of points and the reference constraint values. Bias isthe mean of difference between the predicted constraint values of points and the reference constraint values. Slope and offset are the coefficients of the trend line formula ofthe reference constraint values and the predicted constraint values.


prediction model introducing wavelet transform and genetic algo-rithm technologies was better than other models tested in thestudy. The result of the experiment illustrated that the method isuseful to detect and reconstruct wormholes on oilseed rape leavescaused by cabbage caterpillars.

4. Discussion

The topic of reconstructing oilseed rape leaf attacked by cab-bage caterpillars is discussed in the paper. Wormholes caused bycabbage caterpillars were classified into two kinds, closed and un-closed ones. The closed wormholes reconstructing is relatively easycompared to that for unclosed ones. A novel method was proposedin the study to process the unclosed wormholes. The novel methodincludes wormhole location and reconstruction stages. In the loca-tion stage, location factor based on first derivative of inverse func-tion of parametric equation of the edge curve was proposed todevelop a test function and then the bitten edge was recognizedby observing the pulses of the test function. The position of the bit-ten edge is described by the polar angle of the points of edge. In thereconstruction stage, the G-WNNRA prediction model introducingwavelet transform and genetic algorithm technologies was betterthan other models tested in the study. The modeling data set in-cludes the polar radiuses and polar angles as inputs and constraintvalues as outputs. The constraint value of each point participatingoperation was assigned according to the polar radius of it compar-ing to the point of unbitten edge. Fig. 7 illustrates the result of themethod performed on a certain leaf. The red curves are the pre-dicted edge. Because the contour of the damaged leaf is compli-cated, the reconstruction in Fig. 7 is an ideal result.

Most researches focusing on plant disease and pest damagewere based on the feature of the spectral or hyperspectral informa-tion and the object is analyzed in pixel (Zhao et al., 2011;Abdel-Rahman et al., 2010; Alford et al., 2003). This technology iseffective to detect the damaged tissue when the leaf is intact. Butit is impossible in current study because the leaf is not intact andsome parts have been eaten by pests. The method combining thehyperspectral technology and digital image processing technologyproposed in the current study is more practical.

There are not many researches focusing on damage detectionbased on leaf reconstruction. The research of Zhong et al. (2010)using a leaf reconstruction method to measure pest-damaged areaof leaf based on auto-matching of representative leaf. The superior-ity of the method compared to the method proposed in currentpaper is that it can be used on many kinds of leaves to reconstructthem. But the deficiency is that the reconstruction can be per-formed only when the original undamaged leaf is provided. It isimpossible when the plant has been bitten by pests.

5. Conclusion

In this paper, a novel method was proposed for processingclosed and unclosed wormholes on oilseed rape leaves caused bycabbage caterpillars. The method was composed of closed worm-hole identification, unclosed wormhole location and reconstruc-tion. Closed wormholes were processed by hole filling function.The unclosed wormholes processing method was composed of alocation algorithm and a reconstruction method. In the locationalgorithm, the location factor and test function was used to detectthe bitten edge. Calibration models and validation models wereestablished based on G-WNNRA, WNN, GNN and BPNN algorithms.Correlation coefficient and determination coefficient of calibrationmodel based on G-WNNRA are 0.998 and 0.953, respectively,which are better than the models based on WNN, GNN and BPNNalgorithms. Therefore the proposed leave edge reconstructionmethod is effective. Further work can be focused on the introduc-tion of an expert system into damaged edge predicting and theextension of the method to unknown types of crops. A classifica-tion step which output the wormhole positive/negative value foreach oilseed rape leave have not accomplished in the paper yet,which can be researched in the future.

Acknowledgements

This research was supported by 863 National High-TechResearch and Development Plan (2011AA100705), Science andTechnology Department of Zhejiang Province (2009C12002,2010C31052, 2011C32G2130011, 2011C22070), Zhejiang Provin-cial Natural Science Foundation of China (Y6090718, Y107379).

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A novel algorithm for damage recognition on pest-infested oilseed rape leaves

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