Cotton Diseases

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FACEBOOK/Agriculture Delhi www.facebook.com/AgricultureDelhi Somesh Jha, M.Sc.(Agri.) Plant Pathology is a Delhi based Freelance Agroconsultant who has been active in the sector of Agro, Retail and Agri Business consultancy for more than 17 years and has also served companies like Vishal Retail, Reliance Retail, Aadhaar Retailing(Future Group),Godrej Agrovet Ltd., Pragya (NGO) at various managerial levels. Carried out Capacity Building training programs for a reputed agri input company. Skills training of sales team in paddy and wheat belt such as Sangrur, Bathinda, Hanumangarh, Jaipur and Jodhpur. Carried out Behavioral Training program as a Freelance Trainer with a NSDC partner company, for one of the reputed hybrid seed company. Skills training of sales team in cotton belt of Punjab and Haryana regions

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Cotton is a soft, fluffy staple fiber that grows in a boll, or protective capsule, around the seeds of cotton plants of the genus Gossypium. Cotton is the king of fibres, usually referred as white gold . Current estimates for world production are about 25 million tones annually, accounting for 2.5% of the worldˋs arable land. China is the worldˋs largest producer of cotton, but most of this is used domestically

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In a research of identifying and diagnosing cotton disease, the pattern of disease is important part in that, various features of the images are extracted viz. the color of actual infected image, there are so many diseases occurred on the cotton leaf so the leaf color for different diseases is also different, also there are various other features related to shape of image, also there are different shape of holes are present on the leaf of the image, generally the leaf of infected image have elliptical shape of holes, so calculating the major and minor axis is the major task . The features could be extracted using self organizing feature map together with a back-propagation neural network is used to recognize color of image. This information is used to segment cotton leaf pixels within the image, now image which is under consideration is well analyzed and depending upon this software perform further analysis based on the nature of this image.

Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening

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