An Exploration on the Identification of Plant Leaf Diseases using Image Processing Approach

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1 An Exploration on the Identification of Plant Leaf Diseases using Image Processing Approach Vishal Mani Tiwari&Tarun Gupta Computer Science, RadhaGovind Group of Institutions, Meerut, India Abstract From the ancient years, humans and other social species directly & indirectly dependent on Plants. Plants play an enormous role in human life by providing them food for living, wood for houses and other resources to live life.so, human should take care of plants and agricultural crops. But apart from the human, various natural factors are there that are responsible for destroying the growth of plants like unavailability of accurate plant resources, deficiency of sunlight, weather conditions, lack of expert knowledge for the accurate use of pesticides. The major factor responsible for this destruction of plant growth is diseases. Early detection and accurate identification of diseases can control the spread of infection.in the earlier days, it was not easy to identify the plant diseases but with the advancements of digital technology, it becomes easy to identify plant disease with image processing techniques. In this paper, an exploration is made on the exiting approaches of plant leaf disease detection using image processing approach. Also a discussion is made on the major disease types like fungal, bacterial and viral diseases. Different authors have presented the different approaches for the identification of leaf diseases for the different plant types. Keywords Plant Leaf Diseases,Image Processing, Agriculture crops, Disease Symptoms I. INTRODUCTION India is an agricultural country where in about seventy percentage of the population is dependent on productivity of agricultural crops/plants [1]. Farmers have wide range of diversity to select suitable Fruit and Vegetable crops. But due to increasing disease types in plants/agricultural crops, risk of crops quality and growth is also increasing. Diseases are the natural factor that can cause some serious effects on plants which ultimately reduces productivity, quality and quantity of products [2]. Manual detection of plant diseases just increases the human efforts as it is not easy to check each individual plant. Also the manual detection is not appropriate method. However, the cultivation of these crops for optimum yield and quality product is highly technical. It can be improved with the aid of technological support. To evaluate the seriousness of plant diseases, it is essential to develop advanced, speedy and accurate disease detection technologies.the main part of plant to examine the plant diseases is leaf. The detection and classification of leaf diseases accurately is the key to prevent the agriculture loss [3]. Different plant leaf bears different diseases. There are a list of methods and classifiers to detect plant leaf diseases. The considered methods for plant leaf disease are explained as existing work in section IV.Here, the analysis is made on the plant leaf disease detection using image processing approach. The basic steps of image processing [4] are image acquisition, pre-processing (Image Contrast Enhancement), image segmentation, feature extraction, leaf disease detection & classification. These steps are described as below: Image Acquisition: Image acquisition involves the steps to obtain the plant leaf and capture the high quality images to create the required database. The efficiency of the concept depends upon the quality of database image. So, images should be considered of high quality with RGB color. Image Pre-processing: Image preprocessing involves the steps of image contrast enhancement. Here, the captured image is enhanced to remove the noise from image, and then RGB color image is converted into HSV plane image. Image Segmentation: Image segmentation is applied to simplify the illustration of image with segments so that it can be easily analysed. Image segmentation is performed to segment the disease affected and unaffected portions of the leaf. Feature Extraction: After the segmentation, disease portion from the image is extracted. This leaf diseases area is treated as region of interest for the image processing. Then, further features are extracted based 47

2 on the disease symptoms that are used to detect the disease types. Leaf Disease Detection and classification: Then, classifiers are used for the training and testing of the dataset. These classifiers may be fuzzy logic based, k- nearest neighbour, support vector machine, neural network, etc. These methods are used to classify and detect the diseased and healthy leaves. The basic process of image processing used for plant leaf disease analysis is shown in figure 1. Image Acquisition Image Pre-Processing Image Segmentation Feature Extraction Disease Detection & Classification monitor data and remotely sensed images. Thermograph techniques are depending upon the biomass of the fruit or disease. This technique refers to identifying and classifying between fruit and shrubs and trees. Laser sensors are used for fruit recognitions technique. Image processing and laser based application systems are used for navigating a tractor through the alleyway of a citrus grove. In this way, plant leaf diseases can be analysed with the help of available symptoms. Rest of the paper is organized in the following manner. Section II describes the different disease types with symptoms. Section III explains the literature review and Section IV concludes the paper. II. DIFFERENT DISEASE TYPES AND SYMPTOMS The major categories of plant leaf diseases are based on viral, fungal and bacteria [6]. Here are some common symptoms that should be kept in mind if plant growth seems low. A. Fungal disease symptoms Plant leaf diseases, those caused by fungus are discussed below and shown in Figure 2. e.g. Late blight caused by the fungus. Initially it affects older leaves that look like, watersoaked and spot of grey-green color. Further disease affect increases, affects other leaves also and older spots becomes darken. Figure 1: Basic concept of Image Processing Plant diseases are analyzed with the symptoms available on the leaf. There can be various methods to sense the disease symptoms like Visible Spectroscopy, Light Reflectance, Remote Sensing, Thermograph Techniques, Laser Sensing [5]. Visible spectroscopy techniques applied in soil testing and characterization. There are various methods like NIR reflectance spectroscopy, Raman spectroscopy, VIS, VV, etc. which are applied in agriculture sector. Light reflect work on law of reflection, and the result shows the reflection occurs off a curved surface or off a flat surface. Light reflectance is used for disease classification. Each disease has own fundamental color properties and light reflectance works on the bases of wave length and spectrum. Remote sensing technology is used in agriculture for yield crop estimation. It depends on the spatial resolution of the digital image and it is affected in growing crops estimation. This method is more robust because it's established the relation between yield Figure 2: Fungal Disease Symptoms B. Bacterial disease symptoms The disease is characterized by tiny pale green spots which soon come into view as water- soaked. The lesions enlarge and then appear as dry dead spots as shown in Figure 3. C. Viral disease symptoms Figure 3: Bacterial Disease Symptoms 48

3 Among all plant leaf diseases, those caused by viruses are the most difficult to diagnose. Viruses produce no telltale signs that can be readily observed and often easily confused with nutrient deficiencies and herbicide injury. Aphids, leafhoppers, whiteflies and cucumber beetles insects are common carriers of this disease, e.g. Mosaic Virus, Look for yellow or green stripes or spots on foliage, as shown in Figure 4. Leaves might be wrinkled, curled and growth may be stunted. Figure 4: Viral Disease Symptoms III. LITERATURE REVIEW In this section, the existing work of plant leaf disease detection is presented. Various authors have used the different approaches to detect the leaf diseases for different types of plants and crops etc. The review of these considered approaches is summarised in table 1 with their key features. Sjadojevic et al. [7] have presented the concept of deep convolution neural network (CNN) and fine tuning for the identification of plant leaf diseases. Authors have considered thirteen different types of dataset images with healthy leaf images for the experimentation. Deep learning based Caffe framework has been used along with the set of weights learned on a very large dataset by authors. The core of framework is developed in C++ and provides command line, Python, and MATLAB interfaces. Authors have used the 10- fold cross validation test for the accuracy assessment. The overall results shows the accuracy of 96 % and precision value lie between 91 % to 96 %.Fine-tuning has not shown significant changes in the overall accuracy, but augmentation process had greater influence to achieve respectable results. Naik and Sivappagari [8] have presented the plant leaf disease detection by incorporating the concepts of genetic algorithm, neural network and support vector machine. Here, support vector machine is used for the detection and classification of leaf diseases. Genetic algorithm has been used for the image segmentation. For the experimentation, authors have used the dataset of guava, cotton, beans, moang and lemon leaf images. Accuracy is evaluated for both of SVM and neural network. Authors have evaluated the Neural network based obtained accuracy shows efficient results as compare to SVM. Mohanty et al. [9] have used the concept of deep convolutional neural network for the analysis of plant leaf diseases. Authors have used the PlantVillage dataset having 38 classes based 54, 306 images for the experimentation. This dataset consists of 14 crop species and 26 types of disease affected plants. Deep learning based architecture of AlexNet and GoogLeNet have been considered. Training mechanism of transfer learning and training from scratch approaches have been used. Testing is also performed with different ration aspect of training and testing. Authors have shown the accuracy of % for the disease analysis. But there were some limitations with the concept. Approach is limited to applied dataset and presented approach is not able to detect the leaf diseases if the leaf side changed apart from the front area. Bhog and Pawar [10] have incorporated the concept of neural network for the classification of cotton leaf disease analysis. For segmentation, K-means clustering has been used. Different cotton leaf diseases are like Red spot, white spot, Yellow spot, Alternaria and Cercospora on the Leaf. For experimentation, MATLAB toolbox has been used. The recognition accuracy for K-Mean Clustering method using Euclidean distance is 89.56% and the execution time for K- Mean Clustering method using Euclidean distance is second. Ramakrishnan et al. [11]has used back propagation algorithm for the identification of groundnut leaf diseases. Cercospora is the common groundnut disease. Its further stage is cercosposiumpersonatum, then phaeoisariopsis and final stage is alternaris. This classification with the proposed concept shows efficient results. Singh et al. [12] has presented the existing work for the detection of unhealthy region of plant leaves. Authors have described the framework for the detection and classification of plat leaf diseases. Authors have also performed an important step of image segmentation for leaf disease detection. For segmentation, genetic algorithm is used by the authors and segmented the healthy and unhealthy region of the plants. The overall results are efficient for plant leaf diseases but authors have also suggested to use Bayes classifier, ANN, Fuzzy Logic etc. for the further improvement of concepts. Dandawate and Kokare[13] have used support vector machine concept for the detection and classification of 49

4 soybean plants as diseased or healthy species. Authors have used the SIFT approach that automatically recognizes plant species by their leaf shape. The proposed concept for soybean plant diseases shows an average accuracy of 93.79%. For experimentation, authors have prepared the data manually and launch a mobile application for the farmers. The main objective of the research is to build an autonomous decision support system that can help for the plant leaf diseases information over the mobile internet. Sannakki et al. [14]has used feed forward back propagation Neural Network based technique for the diagnosis and classification of diseases in grape leaf. Author has used the images of grape leaf with complex background for the diagnosis as input. Further anisotropic diffusion is used to remove the noise of the image which is further segmented using k-means clustering. Finally results are observed using neural network. Results are experimented on downy mildew and powdery mildew images with simulation in MATLAB. Confusion matrix is considered with the true positive and false positive parameters for the validation of results. The author claimed to have the training accuracy of 100% if used hue feature alone. Akhtar et al. [15] have used the support vector machine approach for the classification and detection of rose leaf diseases as black spot and anthracnose. Authors have used the threshold method for segmentation and Ostu s algorithm was used to define the threshold values. In this approach, features of DWT, DCT and texture based eleven haralick features are extracted which are further used with SVM approach and shows efficient accuracy value. TABLE I SUMMARIZATION OF PLANT LEAF DISEASE DETECTION APPROACHES Author and Year Type of Plants and Diseases Classification Technique Key Features Sjadojevic et al. (2016) [7] 13 different type of images Deep Convolution Neural Network (CNN) and Fine Tuning Used Deep learning based CaffeNet framework Fine-tuning has not shown significant changes in the overall accuracy. Naik and Sivappagari (2016) [8] Guava, Cotton, Beans, Moang And Lemon Leaf Images Neural Network and Support Vector Machine Neural network based obtained accuracy shows efficient results as compare to SVM Mohanty et al. (2016) [9] PlantVillage dataset having 14 crop species and 26 types of disease Deep Convolutional Neural Network Approach is limited to applied dataset and presented approach is not able to detect the leaf diseases if the leaf side changed apart from the front area. Bhog and Pawar (2016) [10] Cotton Leaf Disease Neural Network Accuracy for K-Mean Clustering method using Euclidean distance is 89.56%. Ramakrishnan et al. (2015) [11] Groundnut leaf diseases Back Propagation Neural Network Discussed the disease of Cercospora and its various stages in groundnut leaf Singh et al. (2015) [12] Different plant leafs Genetic algorithm The overall results of genetic algorithm are efficient for detection of plant leaf diseases but authors have also suggested using Bayes classifier, ANN, Fuzzy Logic etc. for the further improvement of concepts. Dandawate and Kokare Soybean plants Support Vector Machine Soybean plant diseases show an 50

5 (2015) [13] average accuracy of 93.79%. Also a mobile based application launched for soybean disease detection Sannakki et al. (2013) [14] Grape diseases Back Propagation Neural Network Grape diseases viz. Powdery Mildewand Downy Mildew are identified. Accuracy is well Obtained. Akhtar et al. (2013) [15] Rose leaf diseases Support Vector Machine Features of DWT, DCT and texture based eleven haralick features are extracted which are further used with SVM approach and shows efficient accuracy value. IV. CONCLUSIONS Human life is completely dependent upon nature and plants. So, there should be special methods to save plants from diseases. The decrease in crop production also affects the economy of the country. There is the need of appropriate research method that can automatically detect the plant leaf disease. In this research paper, we have determined the different approaches used by different authors for different kind of plant diseases. The considered concept used for classification are SVM, neural network, genetic algorithm, discriminant function approach, back propagation neural network etc. The detected diseases plant are grape, soybean, citrus, grape, groundnut and some other available dataset with their different disease types. From these classification and detection method, there is need to develop some optimized method as existing approaches are not fit for multiple leaf disease types and if any of concept shows efficient results then those results are with limitations. REFERENCES [1]. Arivazhagan, S., R. NewlinShebiah, S. Ananthi, and S. Vishnu Varthini. "Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features." Agricultural Engineering International: CIGR Journal 15, no. 1 (2013): [2]. Kranz, J. "Measuring plant disease." In Experimental techniques in plant disease epidemiology, ISSN no , page no Springer Berlin Heidelberg, [3]. James, W. Clive. "Assessment of plant diseases and losses." Annual Review of Phytopathology Vol. 12, issue no. 1, page no , [4]. Khirade, Sachin D., and A. B. Patil. "Plant Disease Detection Using Image Processing." In Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on, pp IEEE, [5]. Mahlein, Anne-Katrin, Erich-Christian Oerke, Ulrike Steiner, and Heinz-Wilhelm Dehne. "Recent advances in sensing plant diseases for precision crop protection." European Journal of Plant Pathology 133, no. 1 (2012): [6]. Oostendorp, Michael, Walter Kunz, Bob Dietrich, and Theodor Staub. "Induced disease resistance in plants by chemicals." European Journal of Plant Pathology 107, no. 1 (2001): [7]. Sladojevic, Srdjan, Marko Arsenovic, AndrasAnderla, DubravkoCulibrk, and DarkoStefanovic. "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification." Computational Intelligence and Neuroscience 2016 (2016). [8]. Naik, M. Ravindra, and Chandra Mohan Reddy Sivappagari. "Plant Leaf and Disease Detection by Using HSV Features and SVM Classifier." International Journal of Engineering Science 3794 (2016). [9]. Mohanty, Sharada P., David P. Hughes, and Marcel Salathé. "Using Deep Learning for Image-Based Plant Disease Detection." Frontiers in Plant Science 7 (2016). [10]. Bhong, Vijay S., and B. V. Pawar. "Study and Analysis of Cotton Leaf Disease Detection Using Image Processing." International Journal of Advanced Research in Science, Engineering and Technology 3, no. 2 (2016). [11]. Ramakrishnan, M., and A. Nisha. "Groundnut leaf disease detection and classification by using back probagation algorithm." In Communications and Signal Processing (ICCSP), 2015 International Conference 51

6 on, ISNB no , page no IEEE, [12]. Singh, Vijai, and A. K. Misra. "Detection of unhealthy region of plant leaves using image processing and genetic algorithm." In Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in, ISBN no , page no IEEE, [13]. Dandawate, Yogesh, and RadhaKokare. "An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective." In Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on, ISNB no , page no IEEE, [14]. Sannakki, Sanjeev S., Vijay S. Rajpurohit, V. B. Nargund, and ParagKulkarni. "Diagnosis and classification of grape leaf diseases using neural networks." In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, ISBN no , page no IEEE, [15]. Akhtar, Asma, AasiaKhanum, Shoab Ahmed Khan, and ArslanShaukat. "Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques." In Frontiers of Information Technology (FIT), th International Conference on, ISBN no , page no IEEE,