CLASSIFICATION OF WHEAT LEAF SEPTORIA DISEASE USING IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES
Domain:
agricultureRecord type:
paper
Creator:
MEQ
Editor:
Sre
Publisher:
Nat
Host:
Education, health and food security are the three main concerns of developing countries
including Ethiopia, and it’s clear that agriculture is the most powerful factor for the growth
of Ethiopian economy. In addition to this, for citizens in order to keep them alive, at least
planning of food security program is very essential and for these programs to achieve
sufficient productivity of farming fields is expected. One way of making productive field
is the serious care of its elements which begins with growing healthy plants or crops. For
this to achieve a farmer or an agriculture expert should follow up, diagnose a field and
make decisions accordingly. Farmers and agriculture experts visually carry out
examination of crops. However, this evaluation process is tedious, time consuming, and
less accurate, which can cause high risk of loss later. Image processing and machine
learning have been extensively used in various disease diagnosis approaches. It has been
applied to both images captured from cameras of visible light and from equipment that
captures information in invisible wavelength, assisting experts to select the right measure
and treatment. In this research work, a digital camera captured image is used as input and
enhanced with various preprocessing techniques followed by color-based segmentation
method to separate the regions of interest then features are extracted using Gray Level Cooccurrence
Matrix. Classification of the input image is performed at the final stage taking
four different supervised learning algorithms to classify in to two different classes called
‘healthy’ and ‘infected’. All the work is done using Python and supporting libraries. Naïve
Bayes, k-Nearest Neighbor, Support Vector Machines and Random Forest are the
classification algorithms taken for comparison. Based on a confusion matrix evaluation
Random Forest found to be the best with 98.7 % accuracy of classification.
including Ethiopia, and it’s clear that agriculture is the most powerful factor for the growth
of Ethiopian economy. In addition to this, for citizens in order to keep them alive, at least
planning of food security program is very essential and for these programs to achieve
sufficient productivity of farming fields is expected. One way of making productive field
is the serious care of its elements which begins with growing healthy plants or crops. For
this to achieve a farmer or an agriculture expert should follow up, diagnose a field and
make decisions accordingly. Farmers and agriculture experts visually carry out
examination of crops. However, this evaluation process is tedious, time consuming, and
less accurate, which can cause high risk of loss later. Image processing and machine
learning have been extensively used in various disease diagnosis approaches. It has been
applied to both images captured from cameras of visible light and from equipment that
captures information in invisible wavelength, assisting experts to select the right measure
and treatment. In this research work, a digital camera captured image is used as input and
enhanced with various preprocessing techniques followed by color-based segmentation
method to separate the regions of interest then features are extracted using Gray Level Cooccurrence
Matrix. Classification of the input image is performed at the final stage taking
four different supervised learning algorithms to classify in to two different classes called
‘healthy’ and ‘infected’. All the work is done using Python and supporting libraries. Naïve
Bayes, k-Nearest Neighbor, Support Vector Machines and Random Forest are the
classification algorithms taken for comparison. Based on a confusion matrix evaluation
Random Forest found to be the best with 98.7 % accuracy of classification.