IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm
A significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measure...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/5/783 |
| _version_ | 1797475372553469952 |
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| author | Nebojša Andrijević Vlada Urošević Branko Arsić Dejana Herceg Branko Savić |
| author_facet | Nebojša Andrijević Vlada Urošević Branko Arsić Dejana Herceg Branko Savić |
| author_sort | Nebojša Andrijević |
| collection | DOAJ |
| description | A significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measurement and prediction results using the system we developed, which was designed to cover beehive ecosystem. It is equipped with an IoT modular base station that collects a wide range of parameters from sensors on the hive and a bee counter at the hive entrance. Data are sent to the cloud for storage, analysis, and alarm generation. A time-series forecasting model capable of estimating the volume of bee exits and entrances per hour, which simulates dependence between environmental conditions and bee activity, was devised. The applied mathematical models based on recurrent neural networks exhibited high accuracy. A web application for monitoring and prediction displays parameters, measured values, and predictive and analytical alarms in real time. The predictive component utilizes artificial intelligence by applying advanced analytical methods to find correlation between sensor data and the behavioral patterns of bees, and to raise alarms should it detect deviations. The analytical component raises an alarm when it detects measured values that lie outside of the predetermined safety limits. Comparisons of the experimental data with the model showed that our model represents the observed processes well. |
| first_indexed | 2024-03-09T20:43:14Z |
| format | Article |
| id | doaj.art-e6785a39721046e583336c394dc7cbc7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T20:43:14Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e6785a39721046e583336c394dc7cbc72023-11-23T22:53:55ZengMDPI AGElectronics2079-92922022-03-0111578310.3390/electronics11050783IoT Monitoring and Prediction Modeling of Honeybee Activity with AlarmNebojša Andrijević0Vlada Urošević1Branko Arsić2Dejana Herceg3Branko Savić4Faculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, 32000 Čačak, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, 32000 Čačak, SerbiaFaculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, SerbiaHigher Education Technical School of Professional Studies, Školska 1, 21000 Novi Sad, SerbiaA significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measurement and prediction results using the system we developed, which was designed to cover beehive ecosystem. It is equipped with an IoT modular base station that collects a wide range of parameters from sensors on the hive and a bee counter at the hive entrance. Data are sent to the cloud for storage, analysis, and alarm generation. A time-series forecasting model capable of estimating the volume of bee exits and entrances per hour, which simulates dependence between environmental conditions and bee activity, was devised. The applied mathematical models based on recurrent neural networks exhibited high accuracy. A web application for monitoring and prediction displays parameters, measured values, and predictive and analytical alarms in real time. The predictive component utilizes artificial intelligence by applying advanced analytical methods to find correlation between sensor data and the behavioral patterns of bees, and to raise alarms should it detect deviations. The analytical component raises an alarm when it detects measured values that lie outside of the predetermined safety limits. Comparisons of the experimental data with the model showed that our model represents the observed processes well.https://www.mdpi.com/2079-9292/11/5/783IoT monitoringpredictive modelinghoneybees activityprecision beekeeping |
| spellingShingle | Nebojša Andrijević Vlada Urošević Branko Arsić Dejana Herceg Branko Savić IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm Electronics IoT monitoring predictive modeling honeybees activity precision beekeeping |
| title | IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm |
| title_full | IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm |
| title_fullStr | IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm |
| title_full_unstemmed | IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm |
| title_short | IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm |
| title_sort | iot monitoring and prediction modeling of honeybee activity with alarm |
| topic | IoT monitoring predictive modeling honeybees activity precision beekeeping |
| url | https://www.mdpi.com/2079-9292/11/5/783 |
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