PLANT LEAF DISEASE DETECTION USING IMAGE PROCESSING

Transcription

1 International Journal of Recent Innovation in Engineering and Research Scientific Journal Impact Factor by SJIF e- ISSN: PLANT LEAF DISEASE DETECTION USING IMAGE PROCESSING Priyanka Dilip Gawali 1,Dr.G U.Kharat 2 and Prof. S.H.Bodake 3 1,2,3 Sharadchandra Pawar College of Engineering, Otur Pune,Maharashtra,India Abstract This offered Work makes open to, a advance computing technology that has been undergone growth to help the farmer to take higher decision about many aspects of the years produce development process. Due to increased production, exact evaluation & diagnosis is being difficult. There is an production, economic losses as well as reduction in quality and quantity of agricultural products. Now a day s plant diseases discovery has received increasing attention in looking at greatly sized field of the years produce. In this paper we need of simple plant Leaves disease discovery system that would help moves-forward in farming. Early information on the years produce being healthy and disease discovery can help the control of diseases through right business manager s designs. This way of doing will gets better amount production of the years produce. This paper also makes a comparison the benefits and limiting conditions of these possible & unused quality ways of doing. It includes several steps viz. image acquisition, image pre-processing, features extraction and minimum distance classifires. Keywords Disease detection, Image acquisition, pre-processing, features extraction, classification. I. INTRODUCTION India is an agricultural country so 70% populations dependent on agriculture. There is a diversion for selection of suitable crops. Such crops affected by fungi, bacteria, viruses. Therefore to detect diseases of crops & diagnose them is a challenging task for farmers. Various types of diseases found on leaf as well as on other parts of plants. Brief observation of expertise is important approach for identification & detection of plant leaf diseases [12]. In developing countries, farmers may have to go large distances to contact expertise. So that makes consulting experts too costly and time consuming. To determine the exact value of these visually found diseases has not to learn yet because of the intricacy of visual pattern. In agricultural science, most of images are produced by using experimental purpose, these images are captured by digital camera, smart phones etc. Hence to arrange number of experiments on leaf disease detection to extract and analyze the significant content. II. THE PROPOSED APPROACH The basic methods of the proposed vision-based detection algorithm in this paper are shown in below figure 1. First, the images of various leaves are going to capture using a digital camera. Then image-processing techniques are applied to the that acquired images to extract features which are necessary for further analysis. Five steps are as follows: 1) Image Acquisition 2) Image Preprocessing 3) Image Segmentation 4) Feature rights Reserved Page 90

2 Figure 1 The basic procedure of the proposed approach III. METHODOLOGY The detection of plant leaf diseases includes main five stages as shown below. They include image acquisition through digital camera or by web. Image pre-processing includes image enhancing and image segmentation where the affected area is segmented. The next stage is feature extraction and last one is classification. At last stage, the presence of diseases on the plant leaf will be identify. In the initial stage, RGB images of leaf samples were captured. The step-by-step procedure as shown below: 1) Acquisition of RGB image 2) conversion of the input image into color space 3) Segmenting the component 4) Obtaining the useful segment 5) Computing the texture features 6) Configuring the neural networks for recognition. A. Image acquisition The images of various leaves captured by a digital camera with required resolution for better quality. The construction of an image is depend on application. Initially, the digital images are acquired from the circumstances using a digital mobile camera or digital camera and given as input to the identification system. B.Image Pre-processing The image is pre-processing to improve the image data that suppress to undesired distortions & enhancing some image features important for further processing and analysis task. It includes color space conversion, image enhancement, and segmentation. Affected leaf disease area is cropped and then converted to the gray level.in this process the noise is eliminated from the capture image to improve the image quality. C.Image Segmentation Image segmentation is used for simplification of disease affect image into better quality which is more meaningful and easy to analyze. Image segmentation includes features extraction & pattern recognition which are digital image processing features. Conversion of a digital image into different fragments is called image segmentation. In this research we used YCbCr color segmentation method, explanation as follows: Ycbcr color system is a common color space system, which is applied by widely used jpeg image. Y, cb and cr, indicates a luminance component and two color component signals respectively. Ycbcr color space is orthogonal which is important factor of composition of RGB from other factors. Ycbcr color space model is used for image compression. Ycbcr color space used to extract affected leaf color with the purpose of less illumination effects. Available Online at : Page 91

3 Volume: 02 Issue: 04 April 2017 (IJRIER) In YCbCr color model, Y indicates luminance component. Cb, Cr indicates color component. Cb is the difference between the blue component and CR is the difference between the red component. Reasons for using YCbCr: 1. Human eye is sensible in colour and brightness. Hence, there is the need of transform RGB to YCbCr. 2. The YCbCr color space is lumma-independent, results in better performance & quality. 3. YCbCr is utilized in video compression standards such as MPEG and JPEG. 4. Y is stored with high resolution,transmitted at high bandwidth, and two chrominance components (CB and CR).They reduces bandwidth, sub sampled, and compressed or treated separately for improved system efficiency. D.Feature Extraction We used Gabor filter to extract texture feature of input image. With the help of Gabor filters consider GLCM feature for the feature extraction of leaf. We extracted some common features like Area, Perimeter, Contrast, Energy, Homogeneity, major length, minor length[1]. After segmentation the diseased part extracted from input image. In the next step, significant features are extracted and those features can be used to determine the meaning of a given sample. Various methods for feature extraction as discussed below. 1) Texture Analysis Methods Textures are a pattern of non-uniform spatial distribution which focused on the individual pixels that makes an image. Texture means material relation of an image. Properties like uniformity, regularity, density, linearity, directionality, coarseness, phase and frequency play main role in reciatation. a) Statistical: Statistical type include grey-level histogram, grey-level co-occurrence matrix, autocorrelation features, and run length matrices for texture extraction. b) Structural: The structural model of texture is combinations of texture primitives. According to concept, structural texture analysis carried out into two steps i.e. extraction of the texture elements and inference of the placement rule. 2) Texture feature extraction methods These methods are used to extract interesting and relevant features from the input image. When texture feature used for extraction then this method is called as texture feature extraction method. a) Color co-occurrence Method: In statistical texture analysis, the texture features are computed from the statistical distribution of observed combinations of intensities at specified positions relative to each other in the images. Grey Level Co-occurrence Matrices (GLCM) is the statistical method. In early days GLCM method is used generally combination with other methods instead of used individually. It can be applied on different color space for color co-occurrence matrix [4]. Spatial Gray-level Dependence Matrices (SGDM) method is a way of extracting statistical texture features[5]. b) Gabor Filter Method: Texture features that are based on the local power spectrum obtained by a bank of Gabor filters are compared [6]. From this method we extract texture features like Area, Perimeter, Contrast, Energy, Homogeneity, major length, minor length, and mean of gray level. The filter is characterized by a preferred orientation and a preferred spatial frequency [6]. When a small-area patch has a wide variation of features of discrete gray tone, the dominant property of that area is texture [7]. Gabor filters are also known as Gabor wavelets. It can be used by defining a set of radial centre frequencies and orientations.[6]. c) Wavelets Transform: Wavelet transforms is well known method of feature extraction. Currently, it became an important feature to be used in texture classification. Several wavelet transforms are used such as Discrete Wavelet Transforms (DWT), Haar wavelet and Daubechies wavelets. DWT is widely used Available Online at : Page 92

4 wavelet transform.therefore, despite being more complex and slower, wavelet transforms usually produces better features with a higher accuracy [8]. d) Principal Component Analysis: PCA uses orthogonal transformation to convert a set of observations of available correlated variables into a set of values uncorrelated variables called principal components. It is sensitive to the relative scaling of the original variables [15]. PCA is the simplest analysis as true eigenvector-based multivariate analyses; PCA procedure is closely related to factor analysis [8]. 3) Color Moment Methods Color moments are very effective method for color based image analysis. The first & second order moment has been proved to be efficient and effective in representing Color distribution of image[9]. Moment 1: It is called Mean. It provides average Color value in the image. Moment 2: It is called Standard Deviation. The standard deviation is the square root of the variance of the distribution. E) Classifier MATLAB is used for software routine. In which training and testing performed via minimum distance classifier. Some Classification Methods are as follows. TABLE 1 TEXTURE CLASSIFICATION TECHNIQUES COMPARISON Texture Classification Techniques Comparison Sr. No Dis- Technique Advantages Advantages Speed of computing Applicable for a distance increases K-Nearest small dataset which according to numbers 1 Neighbor is not trained. available in training (KNN) samples. 2 Radial Basis Function (RBF) Training phase is faster. Hidden layer is easier to explain For speed factor,the execution is slower. 3 Probabilistic Neural Networks Tolerant of noisy inputs. Instances classified by more than one output Training time is too long. Large complexity of network structure. More storage is required fot training data. 4 Support Vector Machine (SVM) Simple geometrical method.easy to explain and with a better solution. Can be robust. Slow training. Algorithm structure is complex so difficult to understand.large no. support vectors are needed from training set to perform classification task. Table 2 Texture Classification Techniques Comparison Identification Techniques Merit s Demerit s 1.Genetic Algorithm Effective method for a complex problem space, Handle large, complex, non differentiable spaces. Not useful method to find some optima, rather than global and Complications involved in the representation of training/output data Available Online at : Page 93

5 2. Back Propagation Neural Network 3.Principal Component Analysis 4. Probabilistic Neural Network Easy to understand, and can be implemented easily using software simulation Used in frequency domain so weights consider in the form of frequency, Used for variable reductions Frequently change data, tolerant of noisy inputs Complex method, required more time for processing. linear separation of Classes not performed by this method, The largest variances do not correspond to the meaningful axes Network structure is complex and required long training time IV. RESULTS The database under consideration consists of 300 images of leaf disease. Figure 2 shows disease detection by using Image pre processing, Image segmentation & feature extraction in MATLAB software. The image segmentation and feature extraction is followed by the detection and classification process. For texture feature extraction we used GLCM method & for classification we used minimum distance classifier. V. CONCLUSION The present paper reviews and summarizes image processing techniques for several plant species that have been used for recognizing plant diseases. The major techniques for detection of plant diseases are: BPNN, SVM, K-means clustering, and GLCM. The review suggests that this Available Online at : Page 94

6 disease detection technique shows a good potential with an ability to detect plant leaf diseases and some limitations. Therefore, there is scope of improvement in the existing research. VI. ACKNOWLEDGMENT I would like to express my special thanks of gratitude to my guide Dr. G. U. Kharat, Head of Department Prof. M. G. Chincole and M.E.coordinator Prof. S. H. Bodake for their valuable guidance and useful suggestion. REFERENCES [1] NOBUYUKI OTSU, A Threshold Selection Method from Gray-Level Histograms in IEEE TRANSACTIONS ON SYSTREMS, MAN, AND CYBERNETICS, VOL.SMC-9, NO. 1, JANUARY 1979 [2] F. Argenti,L. Alparone,G. Benelli, Fast algorithms for texture analysis using co-occurrence matrices Radar and Signal Processing, IEE Proceedings, vol. 137, Issue 6, pp: , No. 6, December [3] S. Arivazhagan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini, Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features, Commission Internationale du Genie Rural(CIGR) journal, vol. 15, no.1, pp: , March [4] Song Kai, liu zhikun, Su hang, Guo chunhong, A Research of maize disease image recognition of Corn Based on BP Networks, Third International Conference on Measuring Technology and Mechatronics Automation,pp: , Shenyang, China, [5] Prof.Sanjay B. Dhaygude, Mr.Nitin P. Kumbhar, Agricultural plant Leaf Disease Detection Using Image Processing, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, S & S Publication vol. 2, Issue 1, pp: , [6] Simona E. Grigorescu, Nicolai Petkov, and Peter Kruizinga Comparison of Texture Features Based on Gabor Filters in IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 11, NO. 10, OCTOBER [7] ROBERT M. HARALICK,K. SHANMUGAM AND IT SHAK DINSTEIN Texture Features for Image Classification in IEEE Transactions on systems, MAN AND CYBERNETICS Vol. SMC-3 No.6 November 1973 pp [8] P. Revathi, M. Hemalatha, Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques, IEEE International Conference on Emerging Trends in Science, Engineering and Technology, pp , Tiruchirappalli, Tamilnadu, India, [9] Hui Yu, Mingjing Li, Hong-Jiang Zhang, Jufu Feng, Color texture moments for content-based image retrieval, International Conference on Image Processing, 2003 [10] Haiguang Wang, Guanlin Li, Zhanhong Ma, Xiaolong Li, Image Recognition of Plant Diseases Based on Principal Component Analysis and Neural Networks, 8th International Conference on Natural Computation, pp , Chongqing, China, [11] A.Menukaewjinda, P.Kumsawat, K.Attakitmongcol, A.Srikaew, Grape leaf disease detection from color imagery using hybrid intelligent system, Proceedings of electrical Engineering/electronics, Computer, Telecommunications and Information technology (ECTI-CON), vol 1. pp: , Krabi, Thailand, [12] Weizheng, S., Yachun, W., Zhanliang, C., and Hongda, W. (2008). Grading Method of Leaf Spot Disease Based on Image Processing. In Proceedings of the 2008 international Conference on Computer Science and Software Engineering - Volume 06 (December 12-14, 2008). CSSE. IEEE Computer Society, Washington, DC, DOI= Available Online at : Page 95