Please use this identifier to cite or link to this item:
https://idr.l2.nitk.ac.in/jspui/handle/123456789/8238
Title: | Image Based Tomato Leaf Disease Detection |
Authors: | Kumar, A. Vani, M. |
Issue Date: | 2019 |
Citation: | 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, 2019, Vol., , pp.- |
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. |
URI: | http://idr.nitk.ac.in/jspui/handle/123456789/8238 |
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.