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CattleSavior: towards implementing an advanced external disease detection system through deep learning

Citation

Abstract

"Lumpy Skin Disease (LSD), Foot and mouth (FMD), and Infectious Bovine Keratoconjunctivitis (IBK) are some of the most common external diseases of cattle in Bangladesh. Besides, these diseases are highly contagious. Thus, it is important to detect the affected cattle in time and take immediate action. For this reason, we have conducted a survey on (N=26) cattle farmers to understand their views and needs on different external cattle diseases. In this study, we build a modern detection system for these external diseases using a deep Convolutional Neural Network (CNN) with 99% accuracy. Besides, we design and implement a novel mobile application (‘CattleSavior’) incorporating the detection system. This application makes it easier for cattle farmers to detect diseases by capturing an image of the affected area, as it provides instant feedback for the treatment. We have included not only the disease detection feature in the application but also some other necessary features that can become very useful for cattle farmers. Other features include: Information on General Diseases; Information about Vaccines for various Diseases; Vaccination Reminders; Cattle Grooming Guidelines; Generalisation of Cattle Medicine and Veterinary Information. Moreover, we have conducted another survey on (N=11) farmers to evaluate their feedback on the mobile app. "

LC Subject Headings

Description

Cataloged from PDF version of thesis.
Includes bibliographical references (pages no. 76-78).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

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Type

Thesis