Plant disease identification using autoencoder

Plant diseases limit the crop production and have received more attention from experts and farmers. Plant disease identification is carried out by experienced people or needs microscopic identification. However, trained people or professionals are not always available, and the manual approach may le...

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Main Author:	Ong, Janice Aun Nee
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99441/1/JaniceOngAunNeeMKE2021.pdf
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id	my-utm-ep.99441
record_format	uketd_dc
spelling	my-utm-ep.994412023-02-27T04:39:56Z Plant disease identification using autoencoder 2021 Ong, Janice Aun Nee TK Electrical engineering. Electronics Nuclear engineering Plant diseases limit the crop production and have received more attention from experts and farmers. Plant disease identification is carried out by experienced people or needs microscopic identification. However, trained people or professionals are not always available, and the manual approach may lead to bias or errors, costly and time-consuming, especially when some of plant disease symptoms are similar. It has also not easily been understood and identified that attacking crop could be due to parasitic organisms like fungus or bacteria besides the insect. To reduce the damage on the crops, plant disease early detection should be carried out in an automated way for early detection, prevention and control. Many methods have been proposed to do automated detection, but it is not easy to target which feature is the best for the classification. Thus, the objective of this project is to develop an automatic feature extraction method in identifying the severity of two types of plant diseases, namely early blight and late blight, which are caused by microorganism attacks. The main classifier module will be governed by autoencoders as an automatic feature extraction to identify the plant diseases. The MATLAB software was used to develop the autoencoder module. With the data set ready from Plant Village leaf images, this project identified two plant diseases into three severity levels, low, mild and severe at 72.7% accuracy. 2021 Thesis http://eprints.utm.my/id/eprint/99441/ http://eprints.utm.my/id/eprint/99441/1/JaniceOngAunNeeMKE2021.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149764 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ong, Janice Aun Nee Plant disease identification using autoencoder
description	Plant diseases limit the crop production and have received more attention from experts and farmers. Plant disease identification is carried out by experienced people or needs microscopic identification. However, trained people or professionals are not always available, and the manual approach may lead to bias or errors, costly and time-consuming, especially when some of plant disease symptoms are similar. It has also not easily been understood and identified that attacking crop could be due to parasitic organisms like fungus or bacteria besides the insect. To reduce the damage on the crops, plant disease early detection should be carried out in an automated way for early detection, prevention and control. Many methods have been proposed to do automated detection, but it is not easy to target which feature is the best for the classification. Thus, the objective of this project is to develop an automatic feature extraction method in identifying the severity of two types of plant diseases, namely early blight and late blight, which are caused by microorganism attacks. The main classifier module will be governed by autoencoders as an automatic feature extraction to identify the plant diseases. The MATLAB software was used to develop the autoencoder module. With the data set ready from Plant Village leaf images, this project identified two plant diseases into three severity levels, low, mild and severe at 72.7% accuracy.
format	Thesis
qualification_level	Master's degree
author	Ong, Janice Aun Nee
author_facet	Ong, Janice Aun Nee
author_sort	Ong, Janice Aun Nee
title	Plant disease identification using autoencoder
title_short	Plant disease identification using autoencoder
title_full	Plant disease identification using autoencoder
title_fullStr	Plant disease identification using autoencoder
title_full_unstemmed	Plant disease identification using autoencoder
title_sort	plant disease identification using autoencoder
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/99441/1/JaniceOngAunNeeMKE2021.pdf
_version_	1776100599117381632

Plant disease identification using autoencoder

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