Threshold-Based Automated Pest Detection System for Sustainable Agriculture
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912087584800768 |
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| author | Li, Tianle Shu, Jia Chen, Qinghong Abrar, Murad Mehrab Raiti, John |
| author_facet | Li, Tianle Shu, Jia Chen, Qinghong Abrar, Murad Mehrab Raiti, John |
| contents | This paper presents a threshold-based automated pea weevil detection system, developed as part of the Microsoft FarmVibes project. Based on Internet-of-Things (IoT) and computer vision, the system is designed to monitor and manage pea weevil populations in agricultural settings, with the goal of enhancing crop production and promoting sustainable farming practices. Unlike the machine learning-based approaches, our detection approach relies on binary grayscale thresholding and contour detection techniques determined by the pea weevil sizes. We detail the design of the product, the system architecture, the integration of hardware and software components, and the overall technology strategy. Our test results demonstrate significant effectiveness in weevil management and offer promising scalability for deployment in resource-constrained environments. In addition, the software has been open-sourced for the global research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19813 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Threshold-Based Automated Pest Detection System for Sustainable Agriculture Li, Tianle Shu, Jia Chen, Qinghong Abrar, Murad Mehrab Raiti, John Image and Video Processing This paper presents a threshold-based automated pea weevil detection system, developed as part of the Microsoft FarmVibes project. Based on Internet-of-Things (IoT) and computer vision, the system is designed to monitor and manage pea weevil populations in agricultural settings, with the goal of enhancing crop production and promoting sustainable farming practices. Unlike the machine learning-based approaches, our detection approach relies on binary grayscale thresholding and contour detection techniques determined by the pea weevil sizes. We detail the design of the product, the system architecture, the integration of hardware and software components, and the overall technology strategy. Our test results demonstrate significant effectiveness in weevil management and offer promising scalability for deployment in resource-constrained environments. In addition, the software has been open-sourced for the global research community. |
| title | Threshold-Based Automated Pest Detection System for Sustainable Agriculture |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2410.19813 |