July 28, 2025 Thesis Open Access

ANCHINESH MOLLA

MARC21 XML Export

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    <subfield code="a">wheat rust disease, fine tuning, softmax, Convocational neural networks, transfer  learning, deep learning</subfield>
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    <subfield code="a">Detection and Classification of Wheat Rust Disease Using Deep  Learning</subfield>
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    <subfield code="a">&lt;p&gt;The wheat crop is among the most significant staple cereal crops in the world. Ethiopia is first in both area and area output of wheat in sub-Saharan Africa, with a share of 55.5% and 47.82% capacity to become a regional exporter. The most prevalent diseases in wheat are wheat leaf rust, wheat yellow rust, and wheat stem rust. It affects the production and quality of wheat all over the world. Because rust disease spreads rapidly in a matter of days, early detection is a difficult task. The research problem addressed in this study is the lack of accurate and efficient methods for early detection and classification of wheat rust disease. Traditional methods of detecting and classifying wheat rust diseases are time-consuming and less accurate than deep learning techniques. Lack of studies that explore the use of deep learning techniques for the detection and classification of wheat rust disease in Ethiopia are research gaps we identified. The study aimed to fill this gap by exploring the effectiveness of deep learning techniques for this purpose. Adet and Arebaminch agricultural research centers were the institutes in Ethiopia where the images of wheat leaf and stem were taken. A total of 2538 images were gathered and compiled for this study we have 15,318 total dataset after augmentation. The suggested system incorporates image preprocessing elements like resizing and augmentation. To accomplish the key goal in this study, pre-trained VGG-19, ResNet-50, and convolution neural network techniques are employed. Regularization methods like dropout, L1 regularization, and early stopping were proposed in this study to improve the model&amp;#39;s effectiveness and accuracy. The experimental result shows that the test accuracy obtained from the transfer learning models VGG19 and ResNnet_50 achieves an accuracy of 81.7% and 84.8%, respectively by using our dataset. Furthermore, the proposed model outperforms in terms of performance and successfully detects and classifies the examined images with a training accuracy of 97.07.8% and a testing accuracy of 96.23% with the softmax activation function after applying the regularization method. The research contribution of this study is the development of accurate model for detecting and classifying wheat rust disease.&lt;/p&gt;</subfield>
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