Use of machine learning and IoT for monitoring and tracking of livestock
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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| Lenguaje: | English |
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Brac University
2023
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| Acceso en línea: | http://hdl.handle.net/10361/21807 |
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10361-218072023-10-15T21:05:08Z Use of machine learning and IoT for monitoring and tracking of livestock Aunindita, Rudaba Farhin Misbah, Muhammed Shiam Joy, Sibbir Bin Rahman, Md. Ashikur Mahabub, Sad Ibn Mukta, Jannatun Noor Department of Computer Science and Engineering, Brac University Livestock monitoring IoT THI Wi-Fi module Decision Tree Classifier SVM Machine learning Livestock--Handling This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 34-37). Given the present era’s expanding population and rising necessity of dairy products, livestock supervision is one of the issues that is becoming progressively more of a priority. Moreover, periodic cattle health monitoring is crucial for extending the lifetime and maintaining the quality of livestock. Numerous ailments can be conveyed from animals to people, thus it is important to determine the condition and health status of livestock early on. This research analyzes the elements provided by various innovation systems and associated equipment, as well as their advantages and disadvantages. Additionally, we have suggested a real-time interval system for monitoring cattle health that is based on the Internet of Things (IoT). The suggested system would include a multi-sensor board that has been specially built to track various physiological indicators, such as skin temperature, heart rate, and the Temperature Humidity Index (THI) of the environment’s temperature and humidity. Wi-Fi technology will be used to transfer the observed data to the server, where data analytics will be carried out using machine learning (ML) models such as Decision Tree Classifier and Support Vector Machine (SVM) to identify sick animals and forecast cattle health over time so that prompt medical attention may be given. Rudaba Farhin Aunindita Muhammed Shiam Misbah Sibbir Bin Joy Md. Ashikur Rahman Sad Ibn Mahabub B.Sc. in Computer Science 2023-10-15T05:15:11Z 2023-10-15T05:15:11Z ©2022 2022-09-29 Thesis ID 22141035 ID 22141042 ID 22141072 ID 18101654 ID 18301079 http://hdl.handle.net/10361/21807 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. 47 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Livestock monitoring IoT THI Wi-Fi module Decision Tree Classifier SVM Machine learning Livestock--Handling |
| spellingShingle |
Livestock monitoring IoT THI Wi-Fi module Decision Tree Classifier SVM Machine learning Livestock--Handling Aunindita, Rudaba Farhin Misbah, Muhammed Shiam Joy, Sibbir Bin Rahman, Md. Ashikur Mahabub, Sad Ibn Use of machine learning and IoT for monitoring and tracking of livestock |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Mukta, Jannatun Noor |
| author_facet |
Mukta, Jannatun Noor Aunindita, Rudaba Farhin Misbah, Muhammed Shiam Joy, Sibbir Bin Rahman, Md. Ashikur Mahabub, Sad Ibn |
| format |
Thesis |
| author |
Aunindita, Rudaba Farhin Misbah, Muhammed Shiam Joy, Sibbir Bin Rahman, Md. Ashikur Mahabub, Sad Ibn |
| author_sort |
Aunindita, Rudaba Farhin |
| title |
Use of machine learning and IoT for monitoring and tracking of livestock |
| title_short |
Use of machine learning and IoT for monitoring and tracking of livestock |
| title_full |
Use of machine learning and IoT for monitoring and tracking of livestock |
| title_fullStr |
Use of machine learning and IoT for monitoring and tracking of livestock |
| title_full_unstemmed |
Use of machine learning and IoT for monitoring and tracking of livestock |
| title_sort |
use of machine learning and iot for monitoring and tracking of livestock |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21807 |
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| _version_ |
1814307152024567808 |