Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting
The accurate monitoring and management of dairy cattle behavior are critical for improving farm productivity as well as animal welfare and health status. In this paper, we present a self-powered dairy-cattle-behavior monitoring system that harnesses 915 MHz radio-frequency (RF) energy harvesting and...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418136/ |
| _version_ | 1797269135362621440 |
|---|---|
| author | Thai Ha Dang Lionel Nkenyereye Viet-Thang Tran Wan-Young Chung |
| author_facet | Thai Ha Dang Lionel Nkenyereye Viet-Thang Tran Wan-Young Chung |
| author_sort | Thai Ha Dang |
| collection | DOAJ |
| description | The accurate monitoring and management of dairy cattle behavior are critical for improving farm productivity as well as animal welfare and health status. In this paper, we present a self-powered dairy-cattle-behavior monitoring system that harnesses 915 MHz radio-frequency (RF) energy harvesting and bidirectional long short-term memory (Bi-LSTM) networks. The system aims to enable continuous and real-time monitoring of cattle behaviors while eliminating the need for battery replacements. By harvesting RF energy from the surrounding electromagnetic radiation, our system achieves long-term, self-sustainable operation, reducing maintenance efforts and costs. The Bi-LSTM network effectively captures the temporal dependencies and patterns in the collected sensor data, enabling accurate behavior recognition and prediction. Experimental results demonstrate the effectiveness of the proposed system in accurately classifying cattle behaviors, with an overall accuracy of 96.79%. Compared with traditional manual observation methods and battery-dependent systems, our self-powered monitoring system offers enhanced automation, improved welfare monitoring, and increased operational efficiency. The combination of RF energy harvesting, and Bi-LSTM networks affords a promising approach for self-powered and intelligent dairy-cattle-behavior monitoring, facilitating optimized management practices in the dairy industry. |
| first_indexed | 2024-04-25T01:43:34Z |
| format | Article |
| id | doaj.art-c16b80b1c1114ed19d466184d83d19e0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-25T01:43:34Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c16b80b1c1114ed19d466184d83d19e02024-03-08T00:00:20ZengIEEEIEEE Access2169-35362024-01-0112337793379110.1109/ACCESS.2024.336085210418136Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy HarvestingThai Ha Dang0Lionel Nkenyereye1https://orcid.org/0000-0001-6714-4402Viet-Thang Tran2Wan-Young Chung3https://orcid.org/0000-0002-0121-855XDepartment of Artificial Intelligence Convergence, Pukyong National University, Busan, South KoreaAI Convergence Education and Research Group, Busan, South KoreaVietnam Research Institute of Electronics, Informatics and Automation, Ho Chi Minh City, VietnamDepartment of Artificial Intelligence Convergence, Pukyong National University, Busan, South KoreaThe accurate monitoring and management of dairy cattle behavior are critical for improving farm productivity as well as animal welfare and health status. In this paper, we present a self-powered dairy-cattle-behavior monitoring system that harnesses 915 MHz radio-frequency (RF) energy harvesting and bidirectional long short-term memory (Bi-LSTM) networks. The system aims to enable continuous and real-time monitoring of cattle behaviors while eliminating the need for battery replacements. By harvesting RF energy from the surrounding electromagnetic radiation, our system achieves long-term, self-sustainable operation, reducing maintenance efforts and costs. The Bi-LSTM network effectively captures the temporal dependencies and patterns in the collected sensor data, enabling accurate behavior recognition and prediction. Experimental results demonstrate the effectiveness of the proposed system in accurately classifying cattle behaviors, with an overall accuracy of 96.79%. Compared with traditional manual observation methods and battery-dependent systems, our self-powered monitoring system offers enhanced automation, improved welfare monitoring, and increased operational efficiency. The combination of RF energy harvesting, and Bi-LSTM networks affords a promising approach for self-powered and intelligent dairy-cattle-behavior monitoring, facilitating optimized management practices in the dairy industry.https://ieeexplore.ieee.org/document/10418136/Cattle monitoring systemradio-frequency energy harvestingbi-directional long short-term memoryone-dimensional convolutional neural network deep learning |
| spellingShingle | Thai Ha Dang Lionel Nkenyereye Viet-Thang Tran Wan-Young Chung Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting IEEE Access Cattle monitoring system radio-frequency energy harvesting bi-directional long short-term memory one-dimensional convolutional neural network deep learning |
| title | Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting |
| title_full | Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting |
| title_fullStr | Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting |
| title_full_unstemmed | Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting |
| title_short | Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting |
| title_sort | self powered cattle behavior monitoring system using 915 mhz radio frequency energy harvesting |
| topic | Cattle monitoring system radio-frequency energy harvesting bi-directional long short-term memory one-dimensional convolutional neural network deep learning |
| url | https://ieeexplore.ieee.org/document/10418136/ |
| work_keys_str_mv | AT thaihadang selfpoweredcattlebehaviormonitoringsystemusing915mhzradiofrequencyenergyharvesting AT lionelnkenyereye selfpoweredcattlebehaviormonitoringsystemusing915mhzradiofrequencyenergyharvesting AT vietthangtran selfpoweredcattlebehaviormonitoringsystemusing915mhzradiofrequencyenergyharvesting AT wanyoungchung selfpoweredcattlebehaviormonitoringsystemusing915mhzradiofrequencyenergyharvesting |