Paper Title
DETECTION OF LUMPY DISEASE IN COWS
Article Identifiers
Authors
K.Rohit Kumar , K.Vamsi , L.Ganesh , V.Rajesh , D.Pravalika,S.HYMA,P.PRASAD
Keywords
Convolution neural networks,radom forest alogorithm,Resnet model .
Abstract
Lumpy Skin Disease (LSD) poses a significant threat to cattle populations worldwide, leading to substantial economic losses and impacting food security. Early detection and prompt intervention are crucial for controlling the spread of LSD. In this study, we propose a novel approach for the automated detection of LSD in cattle using machine learning techniques. The proposed system utilizes advanced image processing algorithms to analyze highresolution images of cattle skin lesions. Initially, the images are preprocessed to enhance features relevant to LSD detection, such as texture, color, and shape characteristics. Subsequently, a machine learning model, trained on a comprehensive dataset of labeled images, is employed for classification purposes. Several state-of-the-art machine learning algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), are evaluated for their efficacy in discriminating between healthy and LSD-affected cattle. The performance of these algorithms is assessed based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).
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How To Cite (APA)
K.Rohit Kumar, K.Vamsi, L.Ganesh, V.Rajesh, & D.Pravalika,S.HYMA,P.PRASAD (April-2024). DETECTION OF LUMPY DISEASE IN COWS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h480-h486. https://ijnrd.org/papers/IJNRD2404754.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : h480-h486
Other Publication Details
Paper Reg. ID: IJNRD_219412
Published Paper Id: IJNRD2404754
Downloads: 000121982
Research Area: Computer Science & TechnologyÂ
Country: Visakhapatnam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404754.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404754
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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