Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/6751
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSrivastava, A.R.
dc.contributor.authorVenkatesan, M.
dc.date.accessioned2020-03-30T09:46:04Z-
dc.date.available2020-03-30T09:46:04Z-
dc.date.issued2020
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2020, Vol.1054, , pp.17-25en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/6751-
dc.description.abstractNowadays, Tea is commonly used in India as well as in all over the world. Tea is produced in many states of India, i.e., Assam, West Bengal, Tamil Nadu, Karnataka, and so on. But, production of tea is heavily affected by various diseases and pests. There are various kinds of diseases in tea leaves and various pests that can damage the tea crop and affect the tea production. Tea crop damage is reduced by recognizing the tea leaf diseases in an early stage. After detection of the kind of tea leaf diseases, suitable preventive method can be used to reduce the tea crop damage. For the detection of tea leaves diseases, there are different classification methods. Some classification techniques are random forest classifier, k-nearest neighbor classifier, support vector machine classifier, neural network, etc. After training the dataset with classifier, the image of tea leaf is given as an input, the best possible match is found by the classifier system, and diseases are recognized by the classifier system. This project is going to use various classification techniques to recognize and predict the tea leaves disease which helps us to improve the tea production of India. � Springer Nature Singapore Pte Ltd 2020.en_US
dc.titleTea leaf disease prediction using texture-based image processingen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.