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Title: Tea leaf disease prediction using texture-based image processing
Authors: Srivastava, A.R.
Venkatesan, M.
Issue Date: 2020
Citation: Advances in Intelligent Systems and Computing, 2020, Vol.1054, , pp.17-25
Abstract: Nowadays, 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.
Appears in Collections:2. Conference Papers

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