Scarecrow monitoring system:employing mobilenet ssd for enhanced animal supervision
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917709242957824 |
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| author | VS, Balaji AR, Mahi PS, Anirudh Ganapathy M, Manju |
| author_facet | VS, Balaji AR, Mahi PS, Anirudh Ganapathy M, Manju |
| contents | Agriculture faces a growing challenge with wildlife wreaking havoc on crops, threatening sustainability. The project employs advanced object detection, the system utilizes the Mobile Net SSD model for real-time animal classification. The methodology initiates with the creation of a dataset, where each animal is represented by annotated images. The SSD Mobile Net architecture facilitates the use of a model for image classification and object detection. The model undergoes fine-tuning and optimization during training, enhancing accuracy for precise animal classification. Real-time detection is achieved through a webcam and the OpenCV library, enabling prompt identification and categorization of approaching animals. By seamlessly integrating intelligent scarecrow technology with object detection, this system offers a robust solution to field protection, minimizing crop damage and promoting precision farming. It represents a valuable contribution to agricultural sustainability, addressing the challenge of wildlife interference with crops. The implementation of the Intelligent Scarecrow Monitoring System stands as a progressive tool for proactive field management and protection, empowering farmers with an advanced solution for precision agriculture.
Keywords: Machine learning, Deep Learning, Computer Vision, MobileNet SSD |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01435 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scarecrow monitoring system:employing mobilenet ssd for enhanced animal supervision VS, Balaji AR, Mahi PS, Anirudh Ganapathy M, Manju Computer Vision and Pattern Recognition Agriculture faces a growing challenge with wildlife wreaking havoc on crops, threatening sustainability. The project employs advanced object detection, the system utilizes the Mobile Net SSD model for real-time animal classification. The methodology initiates with the creation of a dataset, where each animal is represented by annotated images. The SSD Mobile Net architecture facilitates the use of a model for image classification and object detection. The model undergoes fine-tuning and optimization during training, enhancing accuracy for precise animal classification. Real-time detection is achieved through a webcam and the OpenCV library, enabling prompt identification and categorization of approaching animals. By seamlessly integrating intelligent scarecrow technology with object detection, this system offers a robust solution to field protection, minimizing crop damage and promoting precision farming. It represents a valuable contribution to agricultural sustainability, addressing the challenge of wildlife interference with crops. The implementation of the Intelligent Scarecrow Monitoring System stands as a progressive tool for proactive field management and protection, empowering farmers with an advanced solution for precision agriculture. Keywords: Machine learning, Deep Learning, Computer Vision, MobileNet SSD |
| title | Scarecrow monitoring system:employing mobilenet ssd for enhanced animal supervision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.01435 |