Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografske podrobnosti
Main Authors:	Ghosh, Kawshik Kumar, Islam, MD.Fahim-Ul, Efaz, Abrar Ahsan, Ratul, Md. Wahid Sadiq, Shatil, Md Zaid Hassan Khan
Drugi avtorji:	Chakrabarty, Amitabha
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2023
Teme:	Deep learning Edge devices Mastitis Livestock Machine learning Cognitive learning theory
Online dostop:	http://hdl.handle.net/10361/18377

id	10361-18377
record_format	dspace
spelling	10361-183772023-05-30T21:01:49Z Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices Ghosh, Kawshik Kumar Islam, MD.Fahim-Ul Efaz, Abrar Ahsan Ratul, Md. Wahid Sadiq Shatil, Md Zaid Hassan Khan Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Deep learning Edge devices Mastitis Livestock Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-48). The livestock industry is a vital component of the global economy, with a value estimated at over $1.4 trillion. However, the health of livestock animals is frequently threatened by infectious diseases, which can have serious consequences for the industry and the economy. Bovine mastitis is one such disease that is prevalent and costly to treat. It is caused by bacterial infection of the mammary gland in cows and can have severe impacts on the dairy industry. In developing countries like Bangladesh, where the livestock sector is a significant contributor to the national economy, mastitis is a major concern. It is estimated that this disease costs the dairy industry millions of dollars each year in Bangladesh, due to reduced milk production, increased treatment costs, and culling of infected animals. The economic impact of mastitis can be particularly significant in a country like Bangladesh, where the livestock sector plays a vital role in the economy. In order to overcome this issue, this paper presents a real-time system for detecting mastitis in livestock using Deep Learning and Machine-Learning techniques leveraging edge devices. The proposed system aims to provide a timely and accurate diagnosis of clinical mastitis, ultimately reducing costs and improving the efficiency of treatment. By utilizing deep learning and machine learning techniques, the system is able to analyze data from edge devices and make accurate predictions about the presence of mastitis. This can help farmers and veterinarians identify infected animals and take appropriate action to prevent the spread of the disease. In the proposed system, various Deep Learning and Machine Learning algorithms were utilized for classification, and a comparison was made based on their accuracy and performance. The models that performed best with the highest accuracy were selected for further use. InceptionV3 and Random Forest algorithm were chosen for Deep Learning and Machine Learning, respectively, and had an accuracy of 99.34% and 99% respectively. A review of other papers that have used classification techniques for detecting mastitis shows that the models proposed in this paper have demonstrated better accuracy in the diagnosis of mastitis in livestock. The real-time system for detecting mastitis in livestock presented in this paper has the potential to significantly reduce the economic impact of this disease in the dairy industry of Bangladesh and other developing countries. By providing a timely and accurate diagnosis, the system can help to improve treatment efficiency and protect the health and productivity of livestock animals. In doing so, this system can have positive impacts on the livestock industry and the global economy by improving the health and productivity of livestock animals and reducing the costs associated with mastitis. Kawshik Kumar Ghosh MD.Fahim-Ul-Islam Abrar Ahsan Efaz Md. Wahid Sadiq Ratul Md Zaid Hassan Khan Shatil B. Computer Science 2023-05-30T09:19:33Z 2023-05-30T09:19:33Z 2023 2023-01 Thesis ID 19101057 ID 19101294 ID 19101368 ID 19101194 ID 18201192 http://hdl.handle.net/10361/18377 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Edge devices Mastitis Livestock Machine learning Cognitive learning theory
spellingShingle	Deep learning Edge devices Mastitis Livestock Machine learning Cognitive learning theory Ghosh, Kawshik Kumar Islam, MD.Fahim-Ul Efaz, Abrar Ahsan Ratul, Md. Wahid Sadiq Shatil, Md Zaid Hassan Khan Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Ghosh, Kawshik Kumar Islam, MD.Fahim-Ul Efaz, Abrar Ahsan Ratul, Md. Wahid Sadiq Shatil, Md Zaid Hassan Khan
format	Thesis
author	Ghosh, Kawshik Kumar Islam, MD.Fahim-Ul Efaz, Abrar Ahsan Ratul, Md. Wahid Sadiq Shatil, Md Zaid Hassan Khan
author_sort	Ghosh, Kawshik Kumar
title	Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
title_short	Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
title_full	Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
title_fullStr	Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
title_full_unstemmed	Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
title_sort	real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/18377
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Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices

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