Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection

In agriculture sector, plant leaf diseases detection plays a significant role. Plant leaf detection is important for food security, avoiding economic downturns due to severe plant losses, and avoiding environmental degradation due to inappropriate disease treatment. The image processing consists of...

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Main Author:	Chyntia Jaby, Entuni
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/36564/1/Chyntia%20Jaby%20ak%20Entuni%20ft.pdf
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id	my-unimas-ir.36564
record_format	uketd_dc
spelling	my-unimas-ir.365642023-04-17T08:11:41Z Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection 2021-11-03 Chyntia Jaby, Entuni QA75 Electronic computers. Computer science In agriculture sector, plant leaf diseases detection plays a significant role. Plant leaf detection is important for food security, avoiding economic downturns due to severe plant losses, and avoiding environmental degradation due to inappropriate disease treatment. The image processing consists of image segmentation and image classification are commonly used to extract the infected part from the uninfected part to identify the types of the diseases. Some of the existing methods of segmentation are K-Means, Otsu’s, edge-based segmentation, watershed segmentation, region growing, mean shift, maxflow mincut (MFMC) graph cut and regional colour segmentation. The performance of the previous segmentation methods, on the other hand, is average due to their disadvantages such as sensitive to noise and unable to process image with reflection. The example of the previous classification methods are ResNets, bag of features, artificial neural network (ANN), support vector machine (SVM), AlexNet, probabilistic neural network (PNN), principal component analysis (PCA) and k-nearest neighbour (k-NN) and they also have an average performance. This is due to instability and complexity of the network. Hence, algorithm that performed better is required. Thus, in this study, image segmentation method of Fuzzy C-Means with YCbCr and image classification method of DenseNet-201 to detect plant leaf diseases is proposed. The results show that the proposed method performed better than the previous methods with 96.81% for segmentation as well as 95.11% for classification and it is discovered to be a good fusion of algorithms to detect plant leaf diseases. Universiti Malaysia Sarawak (UNIMAS) 2021-11 Thesis http://ir.unimas.my/id/eprint/36564/ http://ir.unimas.my/id/eprint/36564/1/Chyntia%20Jaby%20ak%20Entuni%20ft.pdf text en validuser masters Universiti Malaysia Sarawak Faculty of Engineering
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Chyntia Jaby, Entuni Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection
description	In agriculture sector, plant leaf diseases detection plays a significant role. Plant leaf detection is important for food security, avoiding economic downturns due to severe plant losses, and avoiding environmental degradation due to inappropriate disease treatment. The image processing consists of image segmentation and image classification are commonly used to extract the infected part from the uninfected part to identify the types of the diseases. Some of the existing methods of segmentation are K-Means, Otsu’s, edge-based segmentation, watershed segmentation, region growing, mean shift, maxflow mincut (MFMC) graph cut and regional colour segmentation. The performance of the previous segmentation methods, on the other hand, is average due to their disadvantages such as sensitive to noise and unable to process image with reflection. The example of the previous classification methods are ResNets, bag of features, artificial neural network (ANN), support vector machine (SVM), AlexNet, probabilistic neural network (PNN), principal component analysis (PCA) and k-nearest neighbour (k-NN) and they also have an average performance. This is due to instability and complexity of the network. Hence, algorithm that performed better is required. Thus, in this study, image segmentation method of Fuzzy C-Means with YCbCr and image classification method of DenseNet-201 to detect plant leaf diseases is proposed. The results show that the proposed method performed better than the previous methods with 96.81% for segmentation as well as 95.11% for classification and it is discovered to be a good fusion of algorithms to detect plant leaf diseases.
format	Thesis
qualification_level	Master's degree
author	Chyntia Jaby, Entuni
author_facet	Chyntia Jaby, Entuni
author_sort	Chyntia Jaby, Entuni
title	Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection
title_short	Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection
title_full	Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection
title_fullStr	Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection
title_full_unstemmed	Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection
title_sort	application of fuzzy c-means with ycbcr and densenet-201 for automated corn leaf disease detection
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Engineering
publishDate	2021
url	http://ir.unimas.my/id/eprint/36564/1/Chyntia%20Jaby%20ak%20Entuni%20ft.pdf
_version_	1783728474901446656

Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection

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