![](/jspui/image/dspace_header.jpg)
Please use this identifier to cite or link to this item:
https://idr.nitk.ac.in/jspui/handle/123456789/14771
Title: | Empirical Study on Multi Convolutional Layer-based Convolutional Neural Network Classifier for Plant Leaf Disease Detection |
Authors: | Sunil C.K. Jaidhar C.D. Patil N. |
Issue Date: | 2020 |
Citation: | 2020 IEEE 15th International Conference on Industrial and Information Systems, ICIIS 2020 - Proceedings , Vol. , , p. 460 - 465 |
Abstract: | Recognizing 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. |
URI: | https://doi.org/10.1109/ICIIS51140.2020.9342729 http://idr.nitk.ac.in/jspui/handle/123456789/14771 |
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.