Cover Image

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...

Full description

Saved in:
Bibliographic Details
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
http://eprints.utm.my/id/eprint/99441/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149764
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.99441
record_format eprints
spelling my.utm.994412023-02-27T04:39:56Z http://eprints.utm.my/id/eprint/99441/ Plant disease identification using autoencoder 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/99441/1/JaniceOngAunNeeMKE2021.pdf Ong, Janice Aun Nee (2021) Plant disease identification using autoencoder. Masters thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149764
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2021
url http://eprints.utm.my/id/eprint/99441/1/JaniceOngAunNeeMKE2021.pdf
http://eprints.utm.my/id/eprint/99441/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149764
_version_ 1758966898426380288
score 13.211869

Similar Items