Thesis Open Access

DEVELOPING BACTERIAL WILT DETECTION MODEL ON ENSET CROP USING A DEEP LEARNING APPROACH

YIDNEKACHEW KIBRU AFEWORK


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    <subfield code="a">Convolutional Neural Network, Deep Learning, Image Classification, Disease detection, Enset Bacterial Wilt.</subfield>
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    <subfield code="a">Vuda Sreenivasa Rao (PhD)</subfield>
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    <subfield code="a">Taye Girma (MSc)</subfield>
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    <subfield code="a">&lt;p&gt;Ethiopia is one of the countries in Africa which have a huge potential for the development of different varieties of crops. There are many cultivated crops which are used as a staple food in different regions of the country. From those crops Enset is the one and which is used by around 15 million peoples as a staple food in central, south, and southwestern regions of Ethiopia. Enset crop is affected by disease caused by bacteria, fungi, and virus. From these, bacterial wilt of Enset is the most determinant constraint to Enset production. Identification of the disease needs special attention from experienced experts in the area and it is not possible for the plant pathologists to reach each and every Enset crop to observe the disease, because the crop is physically big. Thus, developing a computer vision model that can be deployed in drones that automatically identify the disease can help to support the community which cultivates Enset crop. To this end, a deep learning approach for automatic identification of Enset bacterial wilt disease is proposed. The proposed approach has three main phases. The first phase is the collection of healthy and diseased Enset images with the help of agricultural experts from different farms to create a dataset. Then the design of a convolutional neural network that can classify the given image in to healthy and diseased is done. Finally, the designed model is trained and tested by using the collected dataset and compared the designed model with different pre-trained convolutional neural network models namely VGG16 and InceptionV3. The dataset contains 4896 healthy and diseased Enset images. From this, 80% of the images are used for training and the rest for testing the model. During training, data augmentation technique is used to generate more images to fit the proposed model. The experimental result demonstrates that the proposed technique is effective for the identification of Enset bacterial wilt disease. The proposed model can successfully classify the given image with a mean accuracy of 98.5% even though images are captured under challenging conditions such as illumination, complex background, different resolution, and orientation of real scene images.&lt;br&gt;
&amp;nbsp;&lt;/p&gt;</subfield>
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