Bird Repeller Using Opencv
This research addresses one of the major problems in agriculture that farmers are currently facing: birds causing significant crop damage in agricultural fields. To reduce this damage, a person’s presence is often required in the farm field to repel the birds. To eliminate this need and automate the...
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| Published in: | Procedia computer science Vol. 252; pp. 975 - 984 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
2025
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| Subjects: | |
| ISSN: | 1877-0509, 1877-0509 |
| Online Access: | Get full text |
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| Abstract | This research addresses one of the major problems in agriculture that farmers are currently facing: birds causing significant crop damage in agricultural fields. To reduce this damage, a person’s presence is often required in the farm field to repel the birds. To eliminate this need and automate the repellent system, the project aims to develop a bird repellent device using computer vision techniques to detect and repel birds from fields. Existing work lags behind in using effective algorithms and developing hardware. Object detection algorithms like the Haar Cascade Classifier and YOLOv8 are used; YOLOv8 is a powerful object detection algorithm, and it is chosen as the primary algorithm for its accuracy and speed in detecting birds. A hardware device is developed using a Raspberry Pi with a Pi camera and a Bluetooth speaker. Upon detecting birds in the farm fields through the Pi camera, distress calls are produced through the Bluetooth speakers to repel the birds. |
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| AbstractList | This research addresses one of the major problems in agriculture that farmers are currently facing: birds causing significant crop damage in agricultural fields. To reduce this damage, a person’s presence is often required in the farm field to repel the birds. To eliminate this need and automate the repellent system, the project aims to develop a bird repellent device using computer vision techniques to detect and repel birds from fields. Existing work lags behind in using effective algorithms and developing hardware. Object detection algorithms like the Haar Cascade Classifier and YOLOv8 are used; YOLOv8 is a powerful object detection algorithm, and it is chosen as the primary algorithm for its accuracy and speed in detecting birds. A hardware device is developed using a Raspberry Pi with a Pi camera and a Bluetooth speaker. Upon detecting birds in the farm fields through the Pi camera, distress calls are produced through the Bluetooth speakers to repel the birds. |
| Author | Upendra, M. Anuradha, T. Prakash, L. |
| Author_xml | – sequence: 1 givenname: M. surname: Upendra fullname: Upendra, M. email: upendrameesala2527@gmail.com – sequence: 2 givenname: T. surname: Anuradha fullname: Anuradha, T. email: anuradha_it@vrsiddhartha.ac.in – sequence: 3 givenname: L. surname: Prakash fullname: Prakash, L. email: 21it1030@gmail.com |
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| Cites_doi | 10.1063/5.0164559 10.58532/V3BCAG22CH4 10.3390/drones8060259 10.37394/23203.2023.18.14 10.1109/ICICV50876.2021.9388590 10.2991/978-94-6463-482-2_6 10.1088/1742-6596/1477/3/032012 10.1016/j.procs.2023.12.065 10.1109/ACCESS.2024.3420730 10.1109/JSTARS.2023.3339235 |
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| Copyright | 2025 |
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| Keywords | YOLO pi camera Raspberry pi Open CV Cascade Classifier Bird Repellent Computer Vision image detection Distress Call |
| Language | English |
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| References_xml | – reference: Yauri Rodríguez, Ricardo, et al. "Development of an electronic bird repellent system using sound emission." (2023). – reference: Sankaran, Sowmya. "Multi-Species Object Detection in Drone Imagery for Population Monitoring of Endangered Animals." arXiv preprint arXiv:2407.00127 (2024). – volume: 230 start-page: 100 year: 2023 end-page: 111 ident: bib2 article-title: "Wild Animal Detection using YOLOv8" publication-title: Procedia Computer Science – reference: Mashuk, Farhan, Abdus Sattar, and Nahida Sultana. "Machine learning approach for bird detection." 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2021. – reference: . – reference: Protic, Mihajlo, et al. "Signals Intelligence Based Drone Detection Using YOLOv8 Models." Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024). Vol. 113. Springer Nature, 2024. – reference: Krishnan, T., et al. "Bird repeller for agriculture lands and orchards." AIP Conference Proceedings. Vol. 2888. No. 1. AIP Publishing, 2023. – reference: Zamri, Fatin Najihah Muhamad, et al. "Enhanced small drone detection using optimized YOLOv8 with attention mechanisms." IEEE Access (2024). – volume: 2 start-page: 19 year: 2024 end-page: 27 ident: bib9 article-title: "Face mask detection using haar cascades classifier to reduce the risk of Covid-19." International Journal of Mathematics publication-title: Statistics, and Computer Science – volume: 4 start-page: 265 year: 2024 end-page: 271 ident: bib5 article-title: "Computer Vision-Assisted Real-Time Bird Eye Chili Classification Using YOLO V5 Framework" publication-title: Journal of Artificial Intelligence and Technology – volume: 8 start-page: 259 year: 2024 ident: bib3 article-title: Chrysostomos Stylios. "Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage" publication-title: Drones – reference: Roihan, Ahmad, Muhaimin Hasanudin, and Endang Sunandar. "Evaluation methods of bird repellent devices in optimizing crop production in agriculture." Journal of Physics: Conference Series. Vol. 1477. No. 3. IOP Publishing, 2020. – reference: Kale, Manoj, et al. "Damage to the agricultural yield due to birds, present repelling techniques and its impacts: an insight from the Indian perspective." (2012): 49-62. – reference: Yi, Hao, et al. "Small object detection algorithm based on improved YOLOv8 for remote sensing." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023). – reference: Yodha, Kiran, et al. "Birds damage to horticultural crops." (2024). doi: – ident: 10.1016/j.procs.2025.01.058_bib7 doi: 10.1063/5.0164559 – ident: 10.1016/j.procs.2025.01.058_bib14 doi: 10.58532/V3BCAG22CH4 – volume: 8 start-page: 259 issue: 6 year: 2024 ident: 10.1016/j.procs.2025.01.058_bib3 article-title: Chrysostomos Stylios. "Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage" publication-title: Drones doi: 10.3390/drones8060259 – volume: 4 start-page: 265 issue: 3 year: 2024 ident: 10.1016/j.procs.2025.01.058_bib5 article-title: "Computer Vision-Assisted Real-Time Bird Eye Chili Classification Using YOLO V5 Framework" publication-title: Journal of Artificial Intelligence and Technology – ident: 10.1016/j.procs.2025.01.058_bib6 doi: 10.37394/23203.2023.18.14 – ident: 10.1016/j.procs.2025.01.058_bib4 doi: 10.1109/ICICV50876.2021.9388590 – volume: 2 start-page: 19 year: 2024 ident: 10.1016/j.procs.2025.01.058_bib9 article-title: "Face mask detection using haar cascades classifier to reduce the risk of Covid-19." International Journal of Mathematics publication-title: Statistics, and Computer Science – ident: 10.1016/j.procs.2025.01.058_bib11 doi: 10.2991/978-94-6463-482-2_6 – ident: 10.1016/j.procs.2025.01.058_bib12 doi: 10.1088/1742-6596/1477/3/032012 – ident: 10.1016/j.procs.2025.01.058_bib10 – volume: 230 start-page: 100 year: 2023 ident: 10.1016/j.procs.2025.01.058_bib2 article-title: "Wild Animal Detection using YOLOv8" publication-title: Procedia Computer Science doi: 10.1016/j.procs.2023.12.065 – ident: 10.1016/j.procs.2025.01.058_bib1 doi: 10.1109/ACCESS.2024.3420730 – ident: 10.1016/j.procs.2025.01.058_bib8 doi: 10.1109/JSTARS.2023.3339235 – ident: 10.1016/j.procs.2025.01.058_bib13 |
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| SubjectTerms | Bird Repellent Cascade Classifier Computer Vision Distress Call image detection Open CV pi camera Raspberry pi YOLO |
| Title | Bird Repeller Using Opencv |
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