Study on Banana Leaf Disease Identification Using Image Processing Methods

Transcription

1 Study on Banana Leaf Disease Identification Using Image Processing Methods Abstract Disease diagnosis and classification in banana crop using image processing technique is an interesting and useful application for farmers to identify, analyze and manage plant pathogens within fields as effectively and automatically at minimum cost. Major banana diseases express their symptoms on leaf area in their earlier stage of infection. These infections can be analyzed and classified automatically through computer vision and machine vision systems that use image processing techniques for information interpretation. This paper gives a brief review about major banana plant diseases that show symptoms in leaves and explains in detail the image processing techniques that are involved in the process of disease identification in banana leaves. Key words - digital image processing, leaf diseases, banana, pattern classification 1. INTRODUCTION Agriculture is an important sector as it provides food and livelihood for millions of people. It also gives support to the country s economy. Banana is one of the major food cum fruit crops that have capability to provide abundant income to the farmers and country s economy [1]. But this capability of banana crop is not fully enjoyed by the farmers as there are various threats that decrease the production. Major threat in banana production is caused by pests and diseases that reduce crop productivity leading to heavy financial loss for farmers. Diagnosis and identification of plant diseases accurately in early stage facilitate farmers to have a better control over the disease severity [2]. Symptoms of these pests and diseases are varied. In some crops diseases are visible in early stage and in some crops they will be visible only in later stage as there will be no possibility to rescue the crop. Persistent monitoring over the plant helps to identify the pest and disease in early stage and also sustains the 1 Research Scholar, Department of Computer Applications, Bharathiar University, Coimbatore, spayrus@gmail.com 2 Assistant Professor, Department of Computer Applications, Bharathiar University, Coimbatore, jsathee@rediffmail.com D. Surya Prabha 1 & Dr. J. Satheesh Kumar 2 plant quality with minimized yield loss. Many farmers are not aware of the disease identification based on the symptom expression on plants. Hence the support of diagnostic services provided by sources such as agricultural research institutions and state farm advisory services are becoming mandatory for banana cultivators. This always involves more time consumption and need additional cost towards advisory services. Development of an automated system is the need for farmers to avoid these inconveniences and to have a user friendly suggestions [3]. Automation using advanced computer technologies like machine vision, computer vision and image processing provide feasible support to the farmers [4]. In agriculture sector, computer vision and machine vision systems have been developed for various applications like grading of fruits, vegetables, sorting of fruits, vegetables and detection of weeds [5]. Automatic plant disease identification and classification is also an inevitable area that necessitates the development of automated computer vision or machine vision system with the use of image processing technique [6]. In the past decades, different combination of image acquisition methods, image enhancement methods, image segmentation methods and feature extraction methods have been attempted for disease identification and classification. This paper outlines possible diseases that affect the leaves of banana crop and completely reviews different image processing techniques that are used for identification and classification of leaf diseases in banana crop. 2. BANANA LEAF DISEASES Banana crop face various diseases start from its initial stage. Most of the symptoms are expressed on leaf, stem, flowers, fruits, roots and suckers [7]. Major banana diseases that express the symptoms on leaves are panama disease, moko disease, sigatoka disease, black spot, banana bunchy top, infectious chlorosis, banana streak virus and banana bract mosaic virus disease [8] [9]. These leaf diseases are discussed in detail in the following section. 2.1 PANAMA AND MOKO DISEASE Panama disease is caused by a fungi Fusarium oxysporum f.sp. cubense [10]. Symptoms are yellowing of the leaves lower most portions starting from margin to midrib of the leaves. Yellowing region extends upward International Journal of Research in Computer Science and Information Technology (IJRCSIT) 89

2 IJRCSIT I ISSN No. : I Vol. 2 I Issue 2(A) I March leaving heart portion of the leaf alone in green. It is also noted that the leaves have cracks near the base and hangs down in the region of pseudostem as in Fig. 1. Infected rhizomes are the main cause for spread of disease. Moko disease is caused by a bacterium Pseudomonas solanacearum. Symptoms are yellowing of leaves in upward direction. Petiole of the leaves break causes the leaves to hang down as in Fig. 1. Fig. 2 Sigatoka disease, Blackspot disease 2.3 BUNCHY TOP VIRUS, INFECTIOUS CHLOROSIS AND STREAK VIRUS Bananaa bunchy top disease is caused by the banana bunchy top virus. Main symptoms are size of the leaves is reduced and they become fragile and weak as in Fig. 3. Infectious chlorosis is caused by the banana cucumber mosaic virus. Spread of disease is caused by the infected suckers and aphid vectors. Symptoms are appearancee of curls, twists in leaves and new leaves are in vertical upright position as in Fig. 3. Banana streak virus is a common viral disease in banana leaves by having vertical streaks in the leaves and stops the growth of the plant as in Fig. 3. Fig. 1 Panama disease, Moko disease 2.2 SIGATOKA AND BLACK SPOT DISEASES Sigatoka disease is caused by the fungus Mycosphaerella gloeosporioides. Symptoms are small light yellow or brownish green narrow streaks on leaves and it spreads over the entire leaf as in Fig. 2. Black or brown spots are commonly found in the affected leaves. Leaves dry rapidly and cause to defoliate. Black spot disease is caused by the fungus Phyllostictina musarum. It causes dark black or brown spot in the centre of leaves as in Fig. 2. Fig. 3 Bunchy top virus, Infectious chlorosis, Streak virus 3. IMAGE PROCESSING TECHNIQUES IN LEAF DISEASE ANALYSIS Numerous computerized equipments based on image processing techniques are having increasing importance and demand. Image processing methodologies are useful in the development of an automated system for disease diagnosis based on various constraints. Especially, Healthiness of banana crop is witnessed by its green color nature of leaves. Upon infection by fungi, bacteria and virus diseases, the color of the leaf immediately would change from green to yellow, brown or black. Change of color is one of the major criteria used to identify and classify the leaf disease. Different color space model like RGB, HSV, HSI, CIE LAB are available to extract required features accurately for disease identification. Similarly, different feature extraction techniques like gray International Journal of Research in Computer Science and Information Technology (IJRCSIT) 90

3 level co-occurrence matrix (GLCM), neural network and support vector machines are available for disease classification. In this section different image processing techniques that assists in the diagnosis of leaf diseases in banana crop are discussed in detail. 3.1 IMAGE FORMATION Capturing the images of banana leaf is an initial step in disease analysis. Images of the leaves can be captured either as an entire region or as a fractal region based on the analysis. Images of banana leaf will be commonly acquired in RGB color space model. Color is an important feature as color value shows variations based on disease infection in the plant. Other color space models like RGB, HSI, CMYK, L*a*b and HSV can also be used for analysis. Acquired images are manipulated directly in RGB color space model and color transformation is performed over the image to standardize the color model [11]. HSI and HSV color space models are commonly used in leaf disease identification as they are similar to human perception. In HSI color model Hue, Saturation and Intensity of a color is observed and in HSV color model Hue, Saturation and Value of a color is considered for manipulation. Hue is an important color component taken into consideration as it shows the perceived color of an object as in Fig. 4 and. Saturation, Intensity or Value are not considered as it doesn t provide efficient information for analysis as in Fig 4 and (d). image information in an effective manner with proper visual understanding. This process does not change inbuilt information in the leaf image, but it changes dynamic range of specific features for localization. Image resizing and filtering are the common pre-processing techniques used in banana leaf disease diagnosis. Captured leaf image in different resolution sizes are standardized to a fixed resolution size using image resizing. Image filtering is used to remove unwanted region in leaf images as there are more possibilities to have dust particles due drops in leaf area. Filtering process is performed either as low pass or high pass filter. Low pass filter reduces the amplitude of high frequencies and has low frequencies unchanged. High pass filter retains high frequencies and soothes the amplitude of low frequencies. Median filter and Average filter are commonly used filtering techniques to reduce noise in an image and to enhance the quality of an image as in Fig. 5, and. Fig. 5 Input image, Average filtering applied output image, Median filtering applied output image (d) Fig. 4 Input image, Hue component Saturation component (d) Intensity component of HSI color model 3.2 IMAGE PRE-PROCESSING Image pre-processing is a manipulation process performed on an image to provide enhanced information about the infected region in a leaf [12]. It displays leaf 3.3 IMAGE ANALYSIS Image analysis is a method used for quantitative measurements and abstraction of information from an image. Methods like feature extraction, image segmentation, classification and measurements comes under the category of image analysis. The process of separating unwanted regions (background) from region of interest (diseased region) is termed as image segmentation. Boundary based and region based are the two broad categories of image segmentation. Region growing segmentation, watershed, color image segmentation, clustering methods, edge detection methods are some of the image segmentation methods. Commonly used Image segmentation methods in banana leaf disease identification are thresholding, histogram and region growing methods. International Journal of Research in Computer Science and Information Technology (IJRCSIT) 91

4 3.3.1 THRESHOLDING Thresholding is a segmentation method used to separate diseased leaf region from background [13]. Segmentation is done by calculating a threshold value for grouping the pixels into two different groups. One is the diseased region and other is the background region. Background pixels are labeled as 0 (black) and pixels of diseased regions are labeled as 1 (white) as in Fig. 6 and. Selection of threshold value is a critical task in image segmentation. If threshold value selected is small in value, then some background regions are retained in the image along with diseased region. If threshold value selected is high in value, then some parts of diseased region are removed. Selection of optimal threshold provides higher degree of accuracy to segment diseased region from background. identify the diversity of pixels in a leaf image. Variation in pixels distribution helps to segment the diseased region from healthy portion of the leaf. Fig. 7 Histogram representation of Hue value Saturation value and Intensity value for the input image in Fig REGION GROWING Region growing is a method in which regions are formed from a single seed point or from a set of seed points. Seed point is a starting point for a region to grow, as it appends the related neighboring pixels along with it [14]. When there are no pixels related to the seed point for further growth then region growing process is stopped. Resultant region is the output of region growing method as in Fig. 8 and. Success of region growing method is purely based on selection of seed point. Proper selection of seed points help to identify the infected part in a leaf [15]. This might further help to classify the type of disease infection in the plant. Segmentation done by this method has a better result as this method is simple and easier for analysis. Fig. 6 Input image, Output of threshold method HISTOGRAM Histogram is used to analyze the intensity distribution of pixels in an image as in Fig. 7, and. In distribution analysis, the intensity with highest peak value is widely spread over the image. Low peak values present in between two high peak value neighbors are considered as an edge [7]. Selection of optimal threshold value and number of bins determines the higher degree of accuracy in Histogram based segmentation. Histogram helps to International Journal of Research in Computer Science and Information Technology (IJRCSIT) 92

5 and output layer. Accuracy, processing speed and capability of BPNN is determined by the number of hidden layers used in the classification process. BPNN is used to arrive at absolute minimum error in the classification of diseases in the leaves of plant [16]. Fig. 8 Input image, Output of region growing method 4. PATTERN CLASSIFICATION Pattern classification is used in the interpretation of extracted diseased region in an image to identify the type of disease infection in leaves. Classifiers make the process of interpretation in images easier and understandable. Machine learning techniques like minimum distance classifier, support vector machines, artificial neural networks, Principal component analysis and k-nearest neighbor are used in the interpretation of diseases in extracted region. The concept of training data and test data are used in learning techniques to compare the undefined data (test data) with the pre-defined data (training data) set available in the database. Comparison is done to recognize and match the unknown data pattern with the known data pattern for easy identification of the disease infection type in banana leaf. All disease symptoms and its severity level must be properly trained in the training stage of data set for predicting accurate result. These statistical classifiers support farmers for taking control measures in their early stage of disease diagnosis. 4.1 BACK PROPOGATION NEURAL NETWORK Back Propogation Neural Network (BPNN) is a widely used self-training classifier in most of the applications related to image classification. BPNN is capable to adaptively build association between known pattern of input and specific output. It consists of three or more layers with multiple nodes in each layer. Input layer specifies the input features that are used to analyze the diseased region. Output layer specifies the possible disease outcomes in leaf region of a plant. There is one or more hidden layer present in between the input and output layer. These hidden layers provide a linkage between the input 4.2 SUPPORT VECTOR MACHINE Support vector machine is one of the best suitable supervised learning methods used in automatic detection of disease due to its higher accuracy in disease identification. The foremost step is selection of a non linear function to map the extracted disease image to a higher-dimensional space [17]. Construction of hyperplane or set of hyperplanes is useful in the task of mapping and classification. Distance between hyperplane and nearest data point is considered as functional margin. Its higher value produces lesser classification error in automatic identification of diseases. 4.3 PRINCIPAL COMPONENT ANALYSIS Principal component analysis (PCA) is a classical statistical tool used in disease identification. PCA reduces the dimensionality in data by performing transformation. It is a multivariate analysis based on eigen vectors. Covariance matrix and mean vector are generally used as the eigen vectors. Co-occurrence matrix is an image texture analysis technique used to measure the texture of the infected leaf image. This method is derived from the Spatial Gray-level Dependence Matrices. A set of statistical descriptors like correlation, contrast, uniformity, homogeneity and entropy are being used to describe the co-occurrence matrix [18]. 5. CONCLUSION Image processing techniques produces higher degree of accuracy in automatic identification and classification of diseases in banana leaves. These methods eliminate the search for experts on subject matter specialist for disease diagnosis. This reduces the time consumption and cost. This paper explains various leaf diseases and its symptoms in banana plant. Algorithms used for disease identification based on image processing approach are also discussed in detail. This paper also explains importance of pattern classification for better disease classification. REFERENCES [1] Ahmad, M.N.B., Fuad, N.A., Ahmed, S.K., Abidin, A.A.Z., Ali, Z., Yit, W.B., & Sharrif, Z.A.M., (2008). Image Processing of an Agriculture Produce: Determination of Size and Ripeness of a Banana. In: International Symposium on Information Technology Inst. of Elec. and Elec. Eng. Computer Society, Kuala Lumpur, Malaysia, IEEE. 1, 1-7. International Journal of Research in Computer Science and Information Technology (IJRCSIT) 93

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