Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/8238
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dc.contributor.authorKumar, A.
dc.contributor.authorVani, M.
dc.date.accessioned2020-03-30T10:18:15Z-
dc.date.available2020-03-30T10:18:15Z-
dc.date.issued2019
dc.identifier.citation2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, 2019, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8238-
dc.description.abstractLeaf 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.titleImage Based Tomato Leaf Disease Detectionen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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