Image Based Tomato Leaf Disease Detection

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DC Field	Value	Language
dc.contributor.author	Kumar, A.
dc.contributor.author	Vani, M.
dc.date.accessioned	2020-03-30T10:18:15Z	-
dc.date.available	2020-03-30T10:18:15Z	-
dc.date.issued	2019
dc.identifier.citation	2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, 2019, Vol., , pp.-	en_US
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/8238	-
dc.description.abstract	Leaf diseases are the major problem in agricultural sector, which affects crop production as well as economic profit. Early detection of diseases using deep learning could avoid such a disaster. Currently, Convolutional Neural Network (CNN) is a class of deep learning which is widely used for the image classification task. We have performed experiments with the CNN architecture for detecting disease in tomato leaves. We trained a deep convolutional neural network using PlantVillage dataset of 14,903 images of diseased and healthy plant leaves, to identify the type of leaves. The trained model achieves test accuracy of 99.25%. � 2019 IEEE.	en_US
dc.title	Image Based Tomato Leaf Disease Detection	en_US
dc.type	Book chapter	en_US
Appears in Collections:	2. Conference Papers

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