Journal Name:
- Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
Keywords (Original Language):
| Author Name | University of Author | Faculty of Author |
|---|---|---|
Abstract (2. Language):
Crop diseases can affect yield and/or quality of the
harvested commodity. This can influence
profitability and raise the risks of farming. When the
diseases are estimated early, the yield will increase
by taking measures thanks to farmers. The rust
disease is one of the most major crop diseases that
affect crop yield.
Rust disease can be defined as a fungus; it makes the
crops weak by blocking food to the roots and leaves.
It is named “rust” disease, since the spots on the
leaves look like grain of rust which is coloured in
the range of yellow to bright orange, to brown or
red. Some spots have a planar surface, while others
are raised. This disease is infectious amongst
vegetations but not between flowers and vegetables.
The rust firstly seems bright orange. Then, it turns to
dark brown as it proceeds. The infected leaves drop
off and the main stems will show diseased spots as it
spreads. Finally, the crops will die (Dauber 2008).
In general, rust disease can be found in three types
of planting areas. These are yellow rust, brown rust
and black rust. The most common type, called a leaf
or brown rust. This disease is usually seen in the wet
type long leaves. Another common type of rust
disease in plants is called stripe or yellow rust. It is
seen most frequently in the leaves. The last common
type of rust is called black rust and which is the most
destructive kind of rust disease and it causes about
50 % losses per month of crop production efficiency
(Çoklu2011).
In this paper, daylily leaf images are used as crop
sample and derived from different agricultural sites
under expert control and daylily rust disease is
estimated by using GLCM based different classifier
techniques.
Before classification process, the features are
extracted from images with using Gray Level CoOccurrence Matrix (GLCM) method and 7
parameters are derived by this method for each
digital camera image. These parameters are
contrast, correlation, energy, homogeneity, entropy,
standard deviation and mean for first texture feature
vector.
Then, the extracted feature vectors are applied to
different type of classifiers and these vectors are
used as inputs in classification systems. The
Multilayer Perceptron neural network (MLP) , kNearest Neighbor (k -NN) and Least Squares
Support Vector Machine (LS-SVM) classifiers have
been chosen for learning and testing of 53 image
data where 32 of them belongs to class I (normal),
21 of them belongs to class II (rust diseased).
Different structures of networks are tested and the
results are compared in terms of testing
performance for each network model.
Artificial Neural Network (ANN) techniques are
non-linear statistical data modeling or decision
making tools. They can be used to model complex
relationships between inputs and outputs or to find
patterns in data. In pattern recognition, the knearest neighbor algorithm (k-NN) is a method
for classifying objects based on closest training
examples in the feature space. A Least Squares
Support Vector Machine (LS-SVM) is a concept
in computer science for a set of related supervised
learning methods that analyze data and recognize
patterns, used for classification and regression
analysis .These methods were used for classification
system in this paper.
Finally, the best performance was observed as
88.90% in the k-NN and MLP network with 7-5-1
structure. Our results suggest this method is an
accurate and efficient means of estimating daylily
rust disease.
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Abstract (Original Language):
Bitkilerdeki hastalÕklar, hasadÕ ve dolayÕsÕyla verimi etkilemektedir. HastalÕklarÕn önceden tespiti,
çiftçilerin alaca÷Õ önlemler ile verimi artÕracaktÕr. Verimi etkileyen önemli hastalÕklarÕn baúÕnda
pas hastalÕ÷Õ gelmektedir. Bu çalÕúmada bitki örne÷i olarak zambak çiçe÷ine iliúkin yaprak imgeleri
kullanÕlarak, bitkide pas hastalÕ÷ÕnÕn tespiti amaçlanmÕútÕr. ÇalÕúmada kullanÕlan imgeler, zirai
uygulamalarla ilgili farklÕ zirai veri tabanlarÕndan bir uzman yardÕmÕyla elde edilmiútir. Bu
çalÕúmada, GLCM tabanlÕ farklÕ sÕnÕflandÕrÕcÕ teknikleri kullanÕlarak, zambak yapra÷Õnda oluúan
de÷iúimin pas hastalÕ÷Õ olup olmadÕ÷ÕnÕ tespit eden bir sistem tasarlanmÕútÕr.
Zambak yapra÷Õna iliúkin imgelerin gri seviyeli eú-oluúum (GLCM) matrisleri elde edilip,
matrislerin kontrast, korelasyon, enerji, homojenlik ve entropi de÷erleri hesaplanmÕútÕr. øki boyutlu
imgelere iliúkin matrislerden hesaplanan ortalama ve standart sapma de÷erleri öznitelik vektörüne
eklenerek, her imge için toplamda 7 parametre içeren öznitelik vektörü oluúturulmuútur.
Elde edilen öznitelik vektörleri sÕnÕflandÕrÕcÕlar için giriú e÷itim kümesi olarak kullanÕlmÕú ve test
kümesi ile performansÕ en iyi olan sistem belirlenmeye çalÕúÕlmÕútÕr. Bu sistemlerde sÕnÕflandÕrÕcÕ
olarak Çok KatmanlÕ AlgÕlayÕcÕ, k-En YakÕn Komúu (k-NN) ve en küçük kareler Destek Vektör
Makinesi (LS-SVM) yöntemleri kullanÕlmÕútÕr. Zambak çiçe÷i yaprak imgeleri, 32 sa÷lÕklÕ (normal)
imge ve 21 hastalÕklÕ imge olmak üzere toplam 53 örnekten oluúur ve iki (1-Normal, 2-HastalÕklÕ)
grupta sÕnÕflandÕrÕlmÕútÕr. HastalÕ÷Õn tespiti amacÕyla yapÕlan sÕnÕflandÕrma çalÕúmalarÕ sonucunda,
en iyi performansa %88.9 baúarÕ ile GLCM tabanlÕ k-NN ve çok katmanlÕ yapay sinir a÷ÕnÕn (7-5-1)
ulaútÕ÷Õ gözlemlenmiútir. Elde edilen sonuçlar önerilen yöntemin pas hastalÕ÷ÕnÕ tespit etmede
do÷ru ve etkili çalÕútÕ÷ÕnÕ göstermiútir.
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