Please use this identifier to cite or link to this item: https://idr.l4.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

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