Threshold-Based Automated Pest Detection System for Sustainable Agriculture
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 enh...
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| creator | Li, Tianle Shu, Jia Chen, Qinghong Abrar, Murad Mehrab Raiti, John |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2410.19813 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2410_19813</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2410_19813</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2410_198133</originalsourceid><addsrcrecordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMgEKGFpaGBpzMniHZBSlFmfk56ToOiUWp6YoOJaW5OcmlgBZAanFJQouqSWpySWZ-XkKwZXFJam5Cmn5RQrBpcUliZl5iUk5qQqO6UWZyaU5JaVFqTwMrGmJOcWpvFCam0HezTXE2UMXbG18QVFmbmJRZTzI-niw9caEVQAA0CA7HA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Threshold-Based Automated Pest Detection System for Sustainable Agriculture</title><source>arXiv.org</source><creator>Li, Tianle ; Shu, Jia ; Chen, Qinghong ; Abrar, Murad Mehrab ; Raiti, John</creator><creatorcontrib>Li, Tianle ; Shu, Jia ; Chen, Qinghong ; Abrar, Murad Mehrab ; Raiti, John</creatorcontrib><description>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.</description><identifier>DOI: 10.48550/arxiv.2410.19813</identifier><language>eng</language><creationdate>2024-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2410.19813$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2410.19813$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Tianle</creatorcontrib><creatorcontrib>Shu, Jia</creatorcontrib><creatorcontrib>Chen, Qinghong</creatorcontrib><creatorcontrib>Abrar, Murad Mehrab</creatorcontrib><creatorcontrib>Raiti, John</creatorcontrib><title>Threshold-Based Automated Pest Detection System for Sustainable Agriculture</title><description>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.</description><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMgEKGFpaGBpzMniHZBSlFmfk56ToOiUWp6YoOJaW5OcmlgBZAanFJQouqSWpySWZ-XkKwZXFJam5Cmn5RQrBpcUliZl5iUk5qQqO6UWZyaU5JaVFqTwMrGmJOcWpvFCam0HezTXE2UMXbG18QVFmbmJRZTzI-niw9caEVQAA0CA7HA</recordid><startdate>20241017</startdate><enddate>20241017</enddate><creator>Li, Tianle</creator><creator>Shu, Jia</creator><creator>Chen, Qinghong</creator><creator>Abrar, Murad Mehrab</creator><creator>Raiti, John</creator><scope>GOX</scope></search><sort><creationdate>20241017</creationdate><title>Threshold-Based Automated Pest Detection System for Sustainable Agriculture</title><author>Li, Tianle ; Shu, Jia ; Chen, Qinghong ; Abrar, Murad Mehrab ; Raiti, John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2410_198133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Li, Tianle</creatorcontrib><creatorcontrib>Shu, Jia</creatorcontrib><creatorcontrib>Chen, Qinghong</creatorcontrib><creatorcontrib>Abrar, Murad Mehrab</creatorcontrib><creatorcontrib>Raiti, John</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Tianle</au><au>Shu, Jia</au><au>Chen, Qinghong</au><au>Abrar, Murad Mehrab</au><au>Raiti, John</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Threshold-Based Automated Pest Detection System for Sustainable Agriculture</atitle><date>2024-10-17</date><risdate>2024</risdate><abstract>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.</abstract><doi>10.48550/arxiv.2410.19813</doi><oa>free_for_read</oa></addata></record> |
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| title | Threshold-Based Automated Pest Detection System for Sustainable Agriculture |
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