Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/14771
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSunil C.K.
dc.contributor.authorJaidhar C.D.
dc.contributor.authorPatil N.
dc.date.accessioned2021-05-05T10:15:45Z-
dc.date.available2021-05-05T10:15:45Z-
dc.date.issued2020
dc.identifier.citation2020 IEEE 15th International Conference on Industrial and Information Systems, ICIIS 2020 - Proceedings , Vol. , , p. 460 - 465en_US
dc.identifier.urihttps://doi.org/10.1109/ICIIS51140.2020.9342729
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14771-
dc.description.abstractRecognizing the plant disease automatically in real-time by examining a plant leaf image is highly essential for farmers. This work focuses on an empirical study on Multi Convolutional Layer-based Convolutional Neural Network (MCLCNN) classifier to measure the detection efficacy of MCLCNN on recognizing plant leaf image as being healthy or diseased. To achieve this, a set of experiments were conducted with three distinct plant leaf datasets. Each of the experiments were conducted by setting kernel size of 3× 3 and each experiment was conducted independently with different epochs i.e., 50, 75, 100, 125, and 150. The MCLCNN classifier achieved minimum accuracy of 87.47% with 50 epochs and maximum accuracy of 99.25% with 150 epochs for the Peach plant leaves. © 2020 IEEE.en_US
dc.titleEmpirical Study on Multi Convolutional Layer-based Convolutional Neural Network Classifier for Plant Leaf Disease Detectionen_US
dc.typeConference Paperen_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.