Download Project Title: FUNGAL LEAF DISEASE DETECTION

Penang Invention, Innovation And Research Design 2017 (PIID 2017)
Project Title:
FUNGAL LEAF DISEASE DETECTION FOR AGRICULTURAL
STUDENTS
A
Professional
Category
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IPT Students
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Specialization Area
ICT
Mechanical
Eng.
Electrical
Eng.
Civil
Eng.
Chemical
Eng.
Design &
Creativity
Health
Sciences
Social
Sciences
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Noraini Hasan1, Norbahiyah Awang1, Nurbaity Sabri1
Diana Juniza Juanis2, Nabilah Zakaria1
Project Member(s)
Affiliation
Email
Correspondence
1
Faculty of Computer and Mathematical Sciences, Universiti Teknologi
MARA (Melaka), Kampus Jasin, Melaka, Malaysia
2
Faculty of Computer and Mathematical Sciences, Universiti Teknologi
MARA (Negeri Sembilan), Kampus Seremban 3, Negeri Sembilan,
Malaysia
1
[email protected], [email protected],
1
[email protected], [email protected],
1
[email protected]
Noraini Hasan
Faculty of Computer and Mathematical Sciences,
Universiti Teknologi MARA (Melaka), Kampus Jasin,
77300 Merlimau, Melaka , Malaysia.
Tel: +606-2645607, Fax:+ 606-2645016
Penang Invention, Innovation And Research Design 2017 (PIID 2017)
Abstract
Keywords
Early detection of plant disease is very important to prevent serious
outbreak. There are various causes of diseases on plants. One of it is a
fungal infection. This project proposes and experimentally evaluates a
framework of detection and classification of leaf diseases using image
processing techniques and Support Vector Machine (SVM). A few samples
of infected leaves were collected by capturing their images using a digital
camera with the specific calibration procedure under controlled
environment. The classification of the leaf diseases is based on texture
feature extraction using Red Green Blue (RGB) color model. This RGB
pixel color extracted from the identified Region Of Interest (ROI). The
proposed automated classification model employs a classification of SVM
in Matlab for disease classification. The proposed techniques based on
performance results are promising with accuracy.
Disease Detection, Texture Features, K-Means, SVM, GLCM.
Product description This product is developed to identify the disease of the leaves cause by
fungal infection using image processing techniques. The image of infected
leaf is converted to grayscale image. The grayscale image is then segmented
into several clusters, using k-mean clustering. It finds partitions such that
objects within each cluster are as close to each other as possible, and as far
from objects in other clusters as possible. The clustering will extract the
disease area of the leaf. The K-means clustering is set to use squared
Euclidean distance. Gray level concurrences matrices (GLCM) is used to
extract the texture feature of the leaves. This feature is the best for
classification on types of leaf disease. Classification of image is done by
using Support Vector Machine (SVM) and the area of fungal affected leaf
will be calculated based on region of interest cluster image affected leaf.
Lastly, the accuracy is defined as the ratio of correctly recognized image
samples to the total number of samples image tested.
Penang Invention, Innovation And Research Design 2017 (PIID 2017)
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Penang Invention, Innovation And Research Design 2017 (PIID 2017)
Novelty and
uniqueness
This computer application has uniqueness where it is an automated
detection system. Compared to current practice that used naked eyes in
identifying the diseases; this application provides unbiased and more
consistent result. Moreover, this application will not contaminate the plant
as it is only required the image of the infected leaves as input.
Benefit to mankind
This application is developed to facilitate agriculture students to identify the
disease cause by fungal infection. With this application, the diseases can be
detected much faster and much more accurate, compared to current practice.
A consistent and unbiased outcome may result in saving the plant as the
appropriate treatment can be provided to the plant.
Potential
commercialization
This application can play an important role as teaching and learning
equipment especially for agriculture study. With suitable improvement and
appropriate upgrading made on this application, such as making this
application mobile and expanding the types of disease detected, this
application can be a useful tool for the agriculture industry.
Acknowledgment
Penang Invention, Innovation And Research Design 2017 (PIID 2017)
Noraini Hasan is an academician in the Faculty of
Computer and Mathematical Sciences with a
specialisation in mathematics at UiTM (Melaka)
Kampus Alor Gajah. She's holding a Masters of
Mathematical Science from Universiti Teknologi
Malaysia.
Researchers
Biographical Data
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Norbahiyah Awang is an academician in the Faculty
of Computer and Mathematical Sciences with a
specialisation in Mathematics at UiTM (Melaka)
Kampus Jasin. She's holding a Masters of
Mathematical Science from Universiti Kebangsaan
Malaysia.
Nurbaity Sabri is an academician in the Faculty of
Computer and Mathematical Sciences with a
specialization in Image Processing at UiTM (Melaka)
Kampus Jasin. She's holding a Masters of Computer
Science from Universiti Teknologi Malaysia.
Diana Juniza Juanis is an academician in the Faculty
of Computer and Mathematical Sciences with a
specialisation in actuarial sciences at UiTM (Negeri
Sembilan) Kampus Seremban 3. She's holding a
Masters of Actuary Science from City University of
London.
Photo
Nabilah Zakaria is a graduate of the Faculty of
Computer and Mathematical Sciences at UiTM
(Melaka) Jasin. She's holding a Bachelor of computer
science from UiTM (Melaka) Jasin.