DETECTION OF PLANT LEAF DISEASES USING IMAGE SEGMENTATION AGRICULTURE. 5 Dr PM Murali. Tamilnadu, India

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1 Volume 119 No , ISSN: (on-line version) url: DETECTION OF PLANT LEAF DISEASES USING IMAGE SEGMENTATION AGRICULTURE 1 Mr. G.Ramachandran, 2 Mrs A.Malarvizhi, 3 Mr G.Suresh Kumar, 4 Mr S.Kannan, 5 Dr PM Murali 1 Assistant Professor, Department of Electronics and Communication Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation(Deemed to be University) Salem , 2 Assistant Professor, Department of Electronics and Communication Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Mission's Research Foundation (Deemed to be University) Salem , 3 Assistant Professor, Department of Electronics and Communication Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Mission's Research Foundation (Deemed to be University) Salem , 4 Assistant Professor, Department of Electronics and Communication Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Mission's Research Foundation (Deemed to be University) Salem , 5 Assistant Professor, Department of Electronics and Communication Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Mission's Research Foundation,(Deemed to be University) Salem , ABSTRACT Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This project presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using genetic algorithm. Keywords: - Agriculture, Detection Plant, Monitoring 461

2 1. INTRODUCTION The agricultural land mass is more than just being a feeding sourcing in today s world. Indian economy is highly dependent of agricultural productivity. Therefore in field of agriculture, detection of disease in plants plays an important role. To detect a plant disease in very initial stage, use of automatic disease detection technique is beneficial. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. The affected tree has a stunted growth and dies within 6 years. Its impact is found in Alabama, Georgia parts of Southern US. In such scenarios early detection could have been fruitful. 1.1 PLANT DISEASE IDENTIFICATION The plant disease detection is simply naked eye observation by experts through which identification and detection of plant diseases is done. For doing so, a large team of experts as well as continuous monitoring of plant is required, which costs very high when we do with large farms. Plant disease identification by visual way is more laborious task and at the same time, less accurate and can be done only in limited areas. Whereas if automatic detection technique is used it will take less efforts, less time and become more accurate. In plants, some general diseases seen are brown and yellow spots, early and late scorch, and others are fungal, viral and bacterial diseases. Image processing is used for measuring affected area of disease and to determine the difference in the color of the affected area. 1.2 LIMITATIONS OF EXISTING WORK The implementation still lacks in accuracy of result in some cases. More optimization is needed.priori information is needed for segmentation. Database extension is needed in order to reach the more accuracy. Very few diseases have been covered. So, work needs to be extended to cover more diseases. The possible reasons that can lead to misclassification can be as follows: disease symptoms varies from one plant to another, features optimization is needed, more training samples are needed in order to cover more cases and to predict the disease more accurately. 2. THE RESEARCH BACKGROUND The existing method for plant disease detection is simply naked eye observation by experts through which identification and detection of plant diseases is done. For doing so, a large team of experts as well as continuous monitoring of plant is required, which costs very high when we do with large farms. At the same time, in some countries, farmers do not have proper facilities or even idea that they can contact to experts. Due to which consulting experts even cost high as well as time consuming too. In such conditions, the suggested technique proves to be beneficial in monitoring large fields of crops. Automatic detection of the diseases by just seeing the symptoms on the plant leaves makes it easier as well as cheaper. This also supports machine vision to provide image based automatic process control, inspection, and robot guidance. 2.1Detection and classification of plant leaf diseases using image processing techniques: a review Ghaiwat et al. presents survey on different classification techniques that can be used for plant leaf disease classification. For given test example,k-nearest-neighbor method is seems to be suitable as well as simplest of all algorithms for class prediction. If training data is not linearly separable then it is difficult to determine optimal parameters in SVM, which appears as one of its drawbacks.agricultural plant leaf disease detection using image 462

3 processing There are mainly four steps in developed processing scheme, out of which, first one is, for the input RGB image, a color transformation structure is created, because this RGB is used for color generation and transformed or converted image of RGB, that is, HSI is used for color descriptor. In second step, by using threshold value, green pixels are masked and removed. In third, by using pre-computed threshold level, removing of green pixels and masking is done for the useful segments that are extracted first in this step, while image is segment ed. And in last or fourth main step the segmentation is done. 2.2 A review of algorithms for medical image segmentation and their applications to the female pelvic cavity Classified an algorithm on the basis of the principal methodologies.algorithms of each category are discussed and the important ideas,application fields, advantages and disadvantages of each category are summarized. Experiments that use these algorithms to segment the tissues and organs of the female pelvic cavity are to show their unique characteristics. In the last, the important guidelines for designing the segmentation algorithms of the pelvic cavity are proposed. 3.3 Image processing and analysis: applications and trends The computational analysis of images is challenging due to tasks such as segmentation, extraction of representative features, matching,alignment, tracking, motion analysis, deformation estimation, and 3D reconstruction. In paper, the methods for processing and analyzing objects in images and their use in applications like medicine, biomechanics, engineering and materials sciences are proposed. 3.THE RESEARCH METHODOLOGY 3.1 Image Recognition and Segmentation Processes Algorithm Digital camera or similar devices are use to take images of leafs of different types, and then those are used to identify the affected area in leafs. Then different types of image-processing techniques are applied on them, to process those images, to get different and useful features needed for the purpose of analyzing later. Figure 3.1 Compound leaves 463

4 Figure 3.2 Infected leaf Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases. Image segmentation technique which is used for automatic detection and classification of plant leaf diseases. Disease detection in plant leaf is done by using genetic algorithm. Processing the captured image with the image collection same leaf we classify the infected leaf. Based on the infection pattern we can identify the disease Our algorithm provides very solution to prevent the crop and improve the crop output by taking the solution in early stage. 464

5 Figure 3.3 Implementation block diagram 3.2 PROCURING THE INPUT IMAGE In this method, diseased images of plants are captured through the high-resolution camera to create the required database. This database has different types of plant diseases and images are stored in jpeg format. These images are then read in mat lab using read command. 3.3 PRE-PROCESSING THE IMAGE Input image should be pre-processed on performing following steps: 3.4 RGB to Grayscale In RGB each pixel is made up of 3 components i.e., red, green, blue.so more space and time is required for RGB. That s why RGB is converted to gray scale image. 3.5 Resizing of image Images are resized according to the need. For resizing of images nearest neighbor interpolation is used. 3.6 Image filtration This is the process of cleaning up of an image i.e., removal of noises and highlighting some information. Median filters are used. 465

6 4 IMAGE SEGMENTATION According to the region of interest, the image will be divided in to different parts. By this segmentation representation of the image becomes easier to analyze. 4.1 FEATURE EXTRACTION In feature extraction method, various attributes of the segmented image are extracted. Features like color, shape,and texture are extracted using Colourcorrelogram, spatial gray dependency matrix [SGDM]. Color correlogram is used to extract color feature. Correlogram is an image of correlation statistics. SGDM is used to extract texture feature like contrast, energy, local homogeneity, and correlation are computed for the hue content of the image. 4.2 CLASSIFICATION OF DISEASES Classification technique is used for training and testing to detect the type of leaf disease. Classification deals with associating a given input with one of the distinct class. In the given system support vector machine [SVM] is used for classification of leaf disease. The classification process is useful for early detection of disease, identifying the nutrient deficiency. 5 CONCLUSION AND FUTURE ENHANCEMENT Conclusion Our undertaken project is this going to be useful for farmers, horticulturists and to trekkers by providing an useful plant leaf identification system and will eventually identify their diseases. Decision support system is aimed to management of crops from early stage to mature harvest stage involves identification and monitoring. The effect of this improved decision support system will positively influence the production rate of food processing industries In this project detection of plant diseases and classification algorithm using Opencv-python is done. It is the system which identifies the affected part of leaf spot by using image processing techniques. There are two main characteristics of plant disease detection achieved, they are: speed and accuracy. Hence there is a scope for working on development of innovative, efficient & fast interpreting algorithms which will help plant scientist in detecting disease. 5.1 Future Enhancement This major objective is need to help farmer in real time. We need do with the quad chopper with the mounted camera to collect the real time during the flight time and transmit to the server images. Gain the access from the global diseases database to predict the all possible disease and provide the remedy to the user immediately. 6 REFERENCES [1] Savita N. Ghaiwat, ParulArora (2014), Detection and classification of plant leaf diseases using image processing techniques: a review. Int JRecent AdvEngTechnol, 2 (3), pp [2] Sanjay B. Dhaygude, Nitin P. Kumbhar (2013) Agricultural plant leaf disease detection using image processing. Int J Adv Res ElectrElectron InstrumEng, 2 (1) 466

7 [3] R. BadnakheMrunalini, Prashant R. Deshmukh (2011) An application of K-means clustering and artificial intelligence in pattern recognition for crop diseases. IntConfAdvInfTechnol, 20 [4] S. Arivazhagan, R. NewlinShebiah, S. Ananthi, S. Vishnu Varthini (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. AgricEngInt CIGR, 15 (1), pp [5] Anand H. Kulkarni, R.K. AshwinPatil (2012) Applying image processing technique to detect plant diseases. Int J Mod Eng Res, 2 (5), pp [6] Sabah Bashir, Navdeep Sharma (2012) Remote area plant disease detection using image processing. IOSR J Electron CommunEng, 2 (6), pp [7] SmitaNaikwadi, NiketAmoda (2013) Advances in image processing for detection of plant diseases. Int J ApplInnovEng Manage, 2 (11) [8] Sanjay B. Patil (2011). Leaf disease severity measurement using image processing. Int J EngTechnol, 3 (5), pp [9] PiyushChaudhary (2012).Color transform based approach for disease spot detection on plant leaf. IntComputSciTelecommun, 3 (6) [10] A.Ambica,B.Saritha, Gokia Changring, Md. Salman, (2017) Evaluation Of Fluoride Concentration In Groundwater Around Tambaram Taluk, Kanchipuram District, International Journal Of Pure And Applied Mathematics, 116 (13), [11] Arti N. Rathod, BhaveshTanawal, (2013) VatsalShahImage processing techniques for detection of leaf disease. Int J Adv Res ComputSciSoftwEng, 3 (11) 467

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