I Am student of Computer science. "Early detection of disease on leaves by image processing". OCM based robust foliage tracking against various changes in open field. View Praneet Bomma's profile on AngelList, the startup and tech network - Developer - Mumbai - Worked at Mobicule Technologies Pvt. Each disease influences the spectral reflectance of sugar beet tissue in a specific way resulting in disease-specific spectral signatures. To recognize detected portion of leaf through SVM. 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. India is an agricultural country and most of peoples wherein about 70% depends on agricultural. In which initially the infected region is found then different features are extracted such as color, texture and shape. com, 2parul. The proposal. can i use database(sql,mysql or oracle) for storing training features and then compare the test feature with training featureI am confused please give me suggestion. Mariano , 3rd International Conference on Digital Image Processing (ICDIP 2011) , Chengdu, China, 15-17 April 2011. Other image processing techniques can also be used to remove noise from the affected data set. com/a-secure-erasure-codebas. Using image segmentation and classification techniques, the software discriminated disease symptoms from the healthy leaf area. Tree Disease Detection - OpenCV Tutorial 18 Matlab project for PLANT LEAF DISEASE DETECTION USING IMAGE PROCESSING matlab projects Agricultural plant Leaf Disease Detection Using Image. the affected region and accuracy is calculated. nique which is used for automatic detection and classification of plant leaf diseases. Here, a project is proposed with an idea of detection of plant diseases using image processing. Figure Figure1 1 shows one example each from every crop-disease pair from the PlantVillage dataset. The cucumber leaves and their environmental information of Downy mildew, Brown Speck, and Anthracnose were collected for disease recognition. To detect diseased leaf, stem, fruit 2. Sajidullah Khan Abstract: The image processing techniques have become very significant due to the wide spread of computer technology. Nonik Noviana kurniawati and Salwani Abdullah in [1] proposed a method for identifying the paddy diseases. So far, the naked-eye observation of farmers or experts in field is the main approach adopted in practice for detection and identification of apple diseases[4]. Depending upon the image processing result the disease severity is determined and the pesticides are sprayed accordingly. 5 GHz CPu and 4GB ram (NO GPU). Which restrict the growth of plant and quality and quantity of p. The easiest way is to use image processing techniques. If we are able to detect it at the initial stage, we can prevent pest on leaves without spreading all over the field, which reduces the loss of crop and money. Gulhane from electronics and telecommunication dept published in International Journal of Electronics, Communication & Soft Computing. Could you help me what functions or features in OpenCv are possible to use in my. To summarize, we have a total of 60 experimental configurations, which vary on the following parameters: Choice of deep learning architecture: AlexNet, GoogLeNet. Web camera is connected to the pc and. In the proposed disease detection system, the work is carried out on cotton leaves. Plant Leaf Disease Detection Using Image Processing Techniques Abstract- ---Agriculture is the mainstay of the Indian economy. Deoghare Abstract: The main three components are image analyser, feature In the research of identifying and diagnosing leaf diseases using computer vision intellectively in. Keywords— Plant leaf disease; feature extraction; image processing I. Sandeep Singh Kang, 2015 IEEE 3rd International Conference on MOOCs, ], they discussed the study of detection of plant diseases and detection of infected part of plants. The aim of this study is to design, implement and evaluate an image-processing-based software solution for automatic detection and classification of plant leaf diseases. The proposed system is able to detect dental caries with 50-60% success rate. Image Acquisition: The images of the plant leaf are captured through the camera. The image has a format RGB (Red, Green, Blue). Using the concept of Fuzzy set theory, Kole et al. Image processing is best way for detecting and diagnosis the diseases. Shah has published a paper paper ‘leaf disease detection using image processing and neural network’. But in the beginning, there was only the most basic type of image segmentation: thresholding. Camargo et al. Tanawala, and V. Final Year Projects | Fast and Accurate Detection and Classification of Plant Diseases More Details: Visit http://clickmyproject. Automatic detection and analysis of disease are established on. • Calories estimation in food images using Computer Vision • Traffic Management using Vehicle Detection and Number Plate OCR • Plant disease detection using Plant Leaf images • Regression Solution for Sales Data • Find Lost Children Using Deep learning (Face Detection and Recognition). Image Segmentation is the partitioning of the digital image in a multiple segments. Brain tumor is a serious life altering disease condition. Using the concept of Fuzzy set theory, Kole et al. The actual skin detection takes place on Line 41 and 42. The project involves the use of self-designed image processing algorithms and techniques designed using python to segment the disease from the leaf while using the concepts of machine learning to categorise the plant leaves as healthy or infected. Ghaiwat and Parul Arora, “Detection and Classification of Plant Leaf Diseases Using Image processing Techniques: A Review”, International Journal of Recent Advances in Engineering & Technology (IJRAET) Volume-2, Issue - 3, 2014, pp. M and Sahaya Anselin Nisha. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. 2347 – 2812. The main aim is to develop an effective image processing module for early diagnosis of disease, even before symptoms expression, for deadly diseases. The format of all images is jpg. paper we present an automatic detection of plant diseases using image processing techniques. Enhanced images have high quality and clarity than the original image. i am doing apple leaf disease detection project Learn more about k-means clustering, leaf, disease detection, leaf disease detection Statistics and Machine Learning Toolbox, Image Processing Toolbox. Normally to avoid such losses conventional method has done to judge the diseases but it is not an accurate. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection. Here, concept of cuckoo search [2] is considered with support vector machine [3]to optimize the classification parameters of SVM. This will help to design different disease control strategy which will be beneficial in agriculture field. The pattern of disease is important part where some features like the colour of actual infected image are extracted from image. The aim of this research to find the diseases of cotton leaf spot by image processing technique, and analyze the input images by RGB pixel counting and recognize the affected part of leaf spot by Sobel and Canny Edge detection technique and output is obtained. Easily share your publications and get them in front of Issuu's. The project involves the use of self-designed image processing algorithms and techniques designed using python to segment the disease from the leaf while using the concepts of machine learning to categorise the plant leaves as healthy or infected. Identification of symptoms of disease by naked eye is difficult for farmer. A method for determining a type of plant by comparing an image of said plant's leaves with a database of stored images of leaves of which the species is known according to claim 2, wherein the said processing includes any or a combination of: border and line detection algorithms, range and pixel measurement correlation, tree leaf color and pattern detection analysis. 3D reconstruction from Drone Image. This work is to automatically classify whether the black pepper leaf is normal or infected by leaf spot diseases viz. LITERATURE SURVEY Paper [1] implements leaf disease detection using image processing and neural network. March, 2017 Zhang C L, et al. The project presents leaf disease diagnosis using image processing techniques for automated vision system used at agricultural field. The development of technologies for detecting or preventing drowsiness has been done thru several methods, some research used EEG for drowsy detection ,and some used eyeblink sensors,this project uses web camera for Drowsy detection. Page 1 of 1. 5 Abstract— In agriculture sector automatic leaf disease. For studies of plant growth and development, these measurements may be plant height or biomass. In this work we express new technological strategies using mobile captured. This will help to design different disease control strategy which will be beneficial in agriculture field. The traditional methods were inaccurate and not effective. The background briefly describes the identification of projects and related issues. The code is uploaded in the github. The training set includes 55,135 images of disease and healthy plant leaves. Disease detection is the main motive of this system. This research is carried out to study effectiveness of Image Processing and computer vision techniques for detection of disease in sugarcane plants by observing the leaves. Image segmentation, which is an important aspect for disease detection in. The author use an image processing techniques to denoise, to enhance, for segmentation and edge detection in the X-ray image to extract the area, perimeter and shape of nodule. However, the automation of tasks such as object recognition or change detection usually requires image processing techniques. Leaf Disease Detection and Recognition using OpenCV. Few major diseases in sugarcane plant like red rot, mosaic and leaf scald have been studied and detection algorithm for the same has been implemented in this research work. 1091: 10 YEARS OF RADARSAT-2 FLIGHT OPERATIONS: 3396: 3CAT-3/MOTS, AN EXPERIMENTAL NANOSATELLITE FOR MULTISPECTRAL AND GNSS-R EARTH OBSERVATION: AIRBORNE OPTICAL AND GNSS-R CAMPAI. [email protected] Which restrict the growth of plant and quality and quantity of p. The processing system consists of four main steps, first a color transformation structure for the input RGB. Hence, image processing is used for the detection of plant diseases. 2 days ago · The model adapts techniques from the image processing community to fit a model on images of read pileups around candidate variants, using information about the sequence around the candidate variant site to make predictions about the true genotype at the site. Study and analysis of cotton leaf disease detection using image processing work is carried on. image processing of the paddy leaf by histogram is proposed, to avoid large scale effect of these diseases. Add the %#codegen compiler directive to your MATLAB code. Fungi are identified primarily from their morphology, with emphasis placed on their reproductive structures. In this paper an algorithm for plant disease detection using different color models is proposed and tested. The developed methodology consists of two phases. So leaf disease detection is very important research topic. The pattern of disease is important part where some features like the colour of actual infected image are extracted from image. The scale of imaging systems can vary greatly. These image processing steps are image acquisition in RGB color value, image preprocessing using filters, image segmentation using k-medoids clustering, feature extraction with texture statistics, image classification using a Neural network. Aruli & et al… [4]They provides survey on plant leaf disease detection using image processing techniques. It is android based which uses Opencv library. 2 PREVIOUS WORK Using image-processing for disease detection has become popular in medicine, because it is a rapid and re-liable way to assess a patient’s condition. Currently I am working on a project that detects disease in a leaf (spot/discolored), rice leaf in specific. Barbedo, “An automatic method to detect and measure leaf disease symptoms using digital image processing,” Plant Disease, vol. Several algorithms like image enhancement, image filtering which suit for cotton leaf processing were explored in this paper. Therefore in field of agriculture, detection of disease in plants plays an important role. caffe github example. In this research paper, we introduce a practical application of Digital image processing in agriculture for detecting and classifying Brown Spot and frog eye. Next click on Segment Image, then enter the cluster no containing the ROI, i. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. Which restrict the growth of plant and quality and quantity of p. This research provides different methods used to study of leaf disease detection by using image processing technique. In the research paper, "Using Deep Learning for Image-Based Plant Disease Detection," Mohanty and his col-leagues worked with three different versions of the leaf im-ages from PlantVillage. Thresholding: Simple Image Segmentation using OpenCV. Number of crops caused by fungi, bacteria etc. Every spot will be classified into certain disease. This paper proposed a methodology The naked eye observation of experts is the main approach for the analysis and detection of plant leaf diseases used in practice for detection and identification of plant using digital image processing techniques. Install Nvidia driver and Cuda (Optional) If you want to use GPU to accelerate, follow instructions here to install Nvidia drivers, CUDA 8RC and cuDNN 5 (skip caffe installation there). i pant to identify 3 type of disease. After that two different segmentation processes are done to segment the original image and extract useful features to identify the infected parts of the plant leaf. Crop disease detection has been a popular topic of research recently. It Requires Tremendous Amount Of Work, Expertise In The Plant Diseases, And Also Require The Excessive Processing Time. In this article, we will discuss the basics of image processing and digital image processing projects using MATLAB, Python, etc. Crop loss due to diseases is approximates 20to30%. detection technique by using image processing. This proposal of this paper is used to detect the mango disease using image processing techniques with expert. Plant Disease Detection & Classification on Leaf Images using Image Processing Matlab Project with Source Code ABSTRACT Diseases decrease the productivity of plant. Plant diseases detection using image processing techniques Abstract: Agriculture is a most important and ancient occupation in India. Often found in systems that use a CRT to display images [6]. Professor, Department of Electronics & Communication , Mangalore Institute of Technology, Badaga Mijar, Moodabidri-574227 Mangalore. We analyze 54,306 images of plant leaves, which have a spread of 38 class labels assigned to them. Leaf Disease detection using Matlab. How much data do you have? How many images? Are the diseases you want to detect specific to a single species of plant, or a number of them?. handling safety. Keywords-Disease detection, symptoms, neural network, color cooccurrence, plant leaf disease, K-means Method. Ghaiwat, 2Parul Arora GHRCEM, Department of Electronics and Telecommunication Engineering, Wagholi, Pune Email: 1savita. Ghaiwat and Parul Arora, "Detection and Classification of Plant Leaf Diseases Using Image processing Techniques: A Review", International Journal of Recent Advances in Engineering & Technology (IJRAET) Volume-2, Issue - 3, 2014, pp. Automatic detection of plant diseases is essential to automatically detect the symptoms of diseases as early as they appear on the growing stage. and pest relation using wireless sensor network and independent pest and disease dynamics of peanut crops. Gulhane from electronics and telecommunication dept published in International Journal of Electronics, Communication & Soft Computing. Karnataka, INDIA. 2 Figure 1. Plant leaf desease. Thus this technique would be useful for saving the farmers from huge loss. This paper discussed the methods used for the detection of plant diseases using their leaves images. net Abstract-- This paper present survey on different. of Electronics & Telecommunication, Sinhgad Academy of Engineering, Kondhwa (Bk), University of Pune, Pune, India Abstract The study of Plant Diseases refers 2. Using the concept of Fuzzy set theory, Kole et al. The term disease is usually used only for destruction of live plants. Therefore the present study was carried out on automatic disease detection of plant leaf of Phaseolus vulgaris (Beans) and Camellia assamica (Tea) using image processing techniques. Abstract: The aim of this study is to design, implement and evaluate an image-processing-based software solution for automatic detection and classification of plant leaf diseases. This paper proposes a method for disease detection. Yellow Rust Extraction in Wheat Crop based on Color Segmentation Techniques www. A Literature Survey: Plant Leaf Diseases Detection Using Image Processing Techniques processing-based. Easily share your publications and get them in front of Issuu's. Hemalatha[10] presented a paper Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Technique. This paper presents the study of various image processing techniques and applications for pest identification and plant disease detection. Thus this technique would be useful for saving the farmers from huge loss. I'm using OpenCV 3. According to Mohanty and his colleagues, these segmented. Plant diseases are caused by bacteria, fungi, virus, nematodes, etc. Using Digital Camera On Robot To Collect The Data For Leaf Detection. com/a-secure-erasure-codebas. The development of technologies for detecting or preventing drowsiness has been done thru several methods, some research used EEG for drowsy detection ,and some used eyeblink sensors,this project uses web camera for Drowsy detection. Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. But paddy disease likes a Blast; Bacterial Leaf Blight; Rice tungro etc. Install Nvidia driver and Cuda (Optional) If you want to use GPU to accelerate, follow instructions here to install Nvidia drivers, CUDA 8RC and cuDNN 5 (skip caffe installation there). Image processing involves capturing the image and applying various preprocessing techniques and detect the pest in the image. Page 1 of 1. Disease Symptoms Identification In Paddy Leaf Using Image Processing 2221. Gurjar, Viraj A. The number and size of lesions and severity, obtained using the image processing. Initially input images are taken and then image processing is started. This work is to automatically classify whether the black pepper leaf is normal or infected by leaf spot diseases viz. com, 2parul. As a baseline, we train an LSTM for hate speech detection using only the tweets text. Our central area of focus is to have a very accurate prediction as these diseases are deadly and can cause a great harm to human reproductive system. Misra2 1Computer Science Department, IMS Engineering College, Ghaziabad, U. "We have laid our steps in all dimension related to math works. Extract the damaged image form the cotton image in order to measure the damage ratio of the cotton leaf which caused by the diseases or pests. I'll use CNN algorithm for this task. This work presents a review on identification of plant disease using image processing and recognition. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. The Deep Learning methodology, CNN model is developed to perform plant disease detection from leaves images. Detection of Unhealthy Region of Plant Leaves and Classification of Plant Leaf Diseases using Texture Based Clustering Features The recent development of digital camera and growth of data storage has led to a huge amount of image databases. Create improved histogram segmentation method to separate the lesion from normal foliage. Image processing methods for detecting and quantifying rusting areas The core of this research was to develop a systematic Fig. The number and size of lesions and severity, obtained using the image processing. Student 2Assistant Professor 1,2Department of Electronics &Telecommunication Engineering 1,2B. Other image processing techniques can also be used to remove noise from the affected data set. Lots of processes included in medical image processing. Hence, image processing is used for the detection of plant diseases. Detect step edges in an image using piecewise local constant kernel smoothing. A method of mathematics morphology is used to segment these images. This paper presents a neural network algorithmic program for image segmentation technique used for automatic detection still as the classification of plants and survey on completely different diseases classification techniques that may be used for plant leaf disease detection. Currently I am working on a project that detects disease in a leaf (spot/discolored), rice leaf in specific. Leaf Disease Detection and Grading using Image Processing Rahul S. ABSTRACT The urgent need is that many plants are at the risk of extinction. First the images of sugarcane leaf scorch leaves are going to acquire by a latest digital camera. This paper discussed. explored different edge based segmentation methods as Sobel,. Data Set Characteristics:. Diagnosis of Pomegranate Plant Diseases using Neural Network ó IEEE. Disease detection involves the steps. Problem statements describes the problems that arise and make the selected projects to be undertaken. Across globe in many disciplines deep learning has been employed. image processing of the paddy leaf by histogram is proposed, to avoid large scale effect of these diseases. NN’s can be used to increase the recognition rate of classification process. Crop protection especially in large. Feature extraction is performed based on color and texture of the image. : Leaf Disease Detection Using Image Processing Techniques. Detection and Analysis of Plant Leaf Diseases using Image Processing Pushpa Rani M. The actual skin detection takes place on Line 41 and 42. Diseases in crops mostly on the leaves affects on the reduction of both quality and quantity of agricultural products. Abstract: In an agriculture field paddy is one of the major staple foods in the Asian countries like China, India, Indonesia, and Bangladesh etc. and pest relation using wireless sensor network and independent pest and disease dynamics of peanut crops. Now the problem is I am very new to Android programming as well as in OpenCv. Shah has published a paper paper ‘leaf disease detection using image processing and neural network’. [email protected] In most of the cases diseases are seen on the leaves, fruits and stems of the plant, therefore detection of disease plays an important role in successful cultivation of crops. In this video, the plant disease detection application is executed using Django. To recognize detected portion of leaf through SVM. The experimental results validate that the proposed model achieved a test accuracy is found that to around 93% for alternaria alternate, anthracnose, black spot, bacterial blight, cercospora leaf spot diseases in fruit and leaf. In Alexnet 5 convolution layers, 3 fully connected. The techniques involved are image acquisition, converting the RGB images into gray scale images. Automatic detection and analysis of disease are established on. Hello, again! I received the email but I couldn't reply. Convert RGB to CIE L*a*b We use the image data of leaves infected by leaf spot disease. Sazzad Hossain A thesis submitted in partial fulfillment of the requirements for the Bachelors In Science Degree Department of Computer Science and Engineering BRAC University, September 2014. The MATLAB uses snapshot of the leaf samples as an input. Medical Image Processing Projects: Medical Image Processing concepts are developed under matlab simulation. There are two main characteristics of plant disease detection machine-learning methods that must be achieved, they are: speed and accuracy [1]. Sladojevic et al. explored different edge based segmentation methods as Sobel,. The Digital Image Processing - one of the new computer technologiescan be used to detect fungal disease of lotus leaf. choloro = G -(R/2) -(B/2) ###Nitrogen Content. , 2007) classify disease sample images with image color statistical features. Disease Detection (HPCCDD) Algorithm for image analyzing and classification of diseases. Disease Symptoms Identification In Paddy Leaf Using Image Processing 2221. Where can I find a ready-made image dataset for a disease plant leaf for an image processing project? Update Cancel a JNHwO d NCfZV eY b BeYkR y uc Sk L RtQ a HmUA m wX b i d K a u OpsDN L gkBiz a HSY b Bgig s DWWPO. and imaging for citrus disease identification (Belasque Jr. The automatic detection of this disease would lead to effective control in earlier stage of the disease. e 'Anthranose' & 'Blackspot'. A region of interest is a portion of an image that you want to filter or perform some other operation. Compression. Using image processing, we can detect disease easily using various clustering methods. [4] SVM classifier Leaf sample Proposed system is a software solution for automatic detection and classification of plant leaf diseases. Medical Image Processing Projects: Medical Image Processing concepts are developed under matlab simulation. If the diseases are not detected at an. Web camera is connected to the pc and. The author use an image processing techniques to denoise, to enhance, for segmentation and edge detection in the X-ray image to extract the area, perimeter and shape of nodule. Monitoring of health and disease on plant plays an important role in successful cultivation of crops. This work is to automatically classify whether the black pepper leaf is normal or infected by leaf spot diseases viz. The easiest way is to use image processing techniques. (2014) Diagnosis of Sugarcane White Leaf Disease Using the High- ly Sensitive DNA Based Voltammetric Electrochemical Determination. The aim of this study is to design, implement and evaluate an image-processing-based software solution for automatic detection and classification of plant leaf diseases. Automatic detection and analysis of disease are established on. The project involves the use of self-designed image processing algorithms and techniques designed using python to segment the disease from the leaf while using the concepts of machine learning to categorise the plant leaves as healthy or infected. Keywords— Plant leaf disease; feature extraction; image processing I. Presenting a simple, fast, cheap and accurate way for the diagnosis of plant diseases is necessary. Disease Symptoms Identification In Paddy Leaf Using Image Processing 2221. After that classification technique was done using nf toolbox in MATLAt software. R Nagar Bangalore 560054, Karnataka, India Abstract Bean is one of the widely grown crop in the world. This paper is organized into the following sections. In the third step, the texture features for the segmented infected objects were calculated. Objectives: • To detect unhealthy region of plant leaves. First the images of sugarcane leaf scorch leaves are going to acquire by a latest digital camera. Pugoy and V. By this approach one can detect the disease at a very primary stage. The main aim is to develop an effective image processing module for early diagnosis of disease, even before symptoms expression, for deadly diseases. Thresholding: Simple Image Segmentation using OpenCV. In this research paper, we introduce a practical application of Digital image processing in agriculture for detecting and classifying Brown Spot and frog eye. If we are able to detect it at the initial stage, we can prevent pest on leaves without spreading all over the field, which reduces the loss of crop and money. The Internet of Things (IoT) is going to change the agriculture industry and connects the farmers to contend with the challenges they face. [2] Agricultural Plant Leaf Disease Detection Using Image Processing Vision-based detection algorithmwith Masking the green-pixels and Color Co-occurrence Method. Sivasankari G G published on 2018/04/24 download full article with reference data and citations. Diagnosis of Pomegranate Plant Diseases using Neural Network ó IEEE. Image segmentation, which is an important aspect for disease detection in. These features are. Student 2Assistant Professor 1,2Department of Electronics &Telecommunication Engineering 1,2B. The I3a and I3b transformations are developed from a modification of the original I1I2I3 colour transformation to meet the requirements of the plant disease data set. The method for detection and classification of leaf diseases is based on masking and removing of green pixels, applying a specific threshold to extract the infected region and computing the texture statistics to evaluate the diseases using MATLAB. Disease in crops causes significant reduction in quantity and quality of the agricultural product. 2 Figure 1. In the third step, the texture features for the segmented infected objects were calculated. inRange function, supplying our HSV frame, and our lower and upper boundaries as arguments, respectively. Detection and Counting of Blood Cells using Image Segmentation A Review - MATLAB PROJECTS CODE Matlab Projects, Detection and Counting of Blood Cells using Image Segmentation A Review, Segmentation, Red blood Cells(RBCs), White Blood Cells(WBCs), Preprocessing, Matlab Source Code, Matlab Assignment, Matlab Home Work, Matlab Help. I just completed the setup. (2014) Diagnosis of Sugarcane White Leaf Disease Using the High- ly Sensitive DNA Based Voltammetric Electrochemical Determination. Perception of human. Which restrict the growth of plant and quality and quantity of p. Leaf disease segmentation is done by using K-means clustering. Plant Disease Detection & Classification on Leaf Images using Image Processing Matlab Project with Source Code ABSTRACT Diseases decrease the productivity of plant. I am using ASP. The diseased cucumber leaf images were processed by using a series of image pre-processing methods, such as image transforming, smoothing, and segmentation. In this research we focused on detection using RGB color intensity. Microscopic images of biopsy are feature extracted with the Gray Level Co-Occurrence Matrix (GLCM) method and classified using back propagation neural network. so, the crop losses in the developing countries like india which run to billions of dollars affect adversely the country economy and nutritional standard because almost 70% of the population of indian depend on it. The development of technologies for detecting or preventing drowsiness has been done thru several methods, some research used EEG for drowsy detection ,and some used eyeblink sensors,this project uses web camera for Drowsy detection. I was tasked to create an application using the OpenCV and c++ that would take in an image input of a plant leaf. Initially Edge detection based Image segmentation is done, and finally image analysis and classification of. due to natural calamities. Image processing methods for detecting and quantifying rusting areas The core of this research was to develop a systematic Fig. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. Godse in their Detection and classification of plant leaf diseases' paper an android application is used to identify plants spacies and their diseases based on their photographs[4]. In this study an image-processing-based approach is proposed and used for leaf disease detection. Novel feature of L?, a?, Entropy?Density for classifying CLS from soil background. See the complete profile on LinkedIn and discover Qorib’s connections and jobs at similar companies. This is helpful to a farmer to get solution of disease and proper plantation they can achieve Comments and Ratings ( 4 ). If we are able to detect it at the initial stage, we can prevent pest on leaves without spreading all over the field, which reduces the loss of crop and money. It also surveys various leaf diseases and classification techniques that are used for leaf disease detection. Identification of symptoms of disease by naked eye is difficult for farmer. explored different edge based segmentation methods as Sobel,. Shah has published a paper paper ‘leaf disease detection using image processing and neural network’. In this paper consists of two phases to identify the affected part of the disease. We should further research and implement the digital image processing for bet-ter detection of plant diseases. for automatic detection and classification of plant leaf disease. Continue observation of leaf is crutial and effective for exact disease identification. The automatic detection of this disease would lead to effective control in earlier stage of the disease. 5 GHz CPu and 4GB ram (NO GPU). Abstract: Crop disease leaf image segmentation is a key step in crop disease recognition. Hence, image processing is used for the detection of plant diseases. So, overall plant leaf disease detection is presented here. The goal of proposed work is to diagnose the disease using image processing and artificial intelligence techniques on images of grape plant leaf. Leaf diseases can be detected from simple images of the leaves with the help of image processing and segmentation. : Segmentation of Crop Disease Leaf Images Using Fuzzy C-means Clustering Algorithm (in Chinese).