Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
With the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great damage to crops, subst...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/18/9397 |
| _version_ | 1827663609944080384 |
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| author | Bruno Cardoso Catarina Silva Joana Costa Bernardete Ribeiro |
| author_facet | Bruno Cardoso Catarina Silva Joana Costa Bernardete Ribeiro |
| author_sort | Bruno Cardoso |
| collection | DOAJ |
| description | With the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great damage to crops, substantial environmental impact, and unnecessary costs both in material and manpower. Despite the potential of new technologies, pest monitoring is still done in a traditional way, leading to excessive costs, lack of precision, and excessive use of human labour. In this paper, we present an Internet of Things (IoT) network combined with intelligent Computer Vision (CV) techniques to improve pest monitoring. First, we propose to use low-cost cameras at the edge that capture images of pest traps and send them to the cloud. Second, we use deep neural models, notably R-CNN and YOLO models, to detect the Whitefly (WF) pest in yellow sticky traps. Finally, the predicted number of WF is analysed over time and results are accessible to farmers through a mobile app that allows them to visualise the pest in each specific field. The contribution is to make pest monitoring autonomous, cheaper, data-driven, and precise. Results demonstrate that, by combining IoT, CV technology, and deep models, it is possible to enhance pest monitoring. |
| first_indexed | 2024-03-10T00:47:06Z |
| format | Article |
| id | doaj.art-76482aa1aaee43cf9745c536cf44b322 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:47:06Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-76482aa1aaee43cf9745c536cf44b3222023-11-23T14:58:00ZengMDPI AGApplied Sciences2076-34172022-09-011218939710.3390/app12189397Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring NetworkBruno Cardoso0Catarina Silva1Joana Costa2Bernardete Ribeiro3Center for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, PortugalCenter for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, PortugalCenter for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, PortugalCenter for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, PortugalWith the increase of smart farming in the agricultural sector, farmers have better control over the entire production cycle, notably in terms of pest monitoring. In fact, pest monitoring has gained significant importance, since the excessive use of pesticides can lead to great damage to crops, substantial environmental impact, and unnecessary costs both in material and manpower. Despite the potential of new technologies, pest monitoring is still done in a traditional way, leading to excessive costs, lack of precision, and excessive use of human labour. In this paper, we present an Internet of Things (IoT) network combined with intelligent Computer Vision (CV) techniques to improve pest monitoring. First, we propose to use low-cost cameras at the edge that capture images of pest traps and send them to the cloud. Second, we use deep neural models, notably R-CNN and YOLO models, to detect the Whitefly (WF) pest in yellow sticky traps. Finally, the predicted number of WF is analysed over time and results are accessible to farmers through a mobile app that allows them to visualise the pest in each specific field. The contribution is to make pest monitoring autonomous, cheaper, data-driven, and precise. Results demonstrate that, by combining IoT, CV technology, and deep models, it is possible to enhance pest monitoring.https://www.mdpi.com/2076-3417/12/18/9397computer visionpest monitoringInternet of Thingssmart farmingdeep learning |
| spellingShingle | Bruno Cardoso Catarina Silva Joana Costa Bernardete Ribeiro Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network Applied Sciences computer vision pest monitoring Internet of Things smart farming deep learning |
| title | Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network |
| title_full | Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network |
| title_fullStr | Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network |
| title_full_unstemmed | Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network |
| title_short | Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network |
| title_sort | internet of things meets computer vision to make an intelligent pest monitoring network |
| topic | computer vision pest monitoring Internet of Things smart farming deep learning |
| url | https://www.mdpi.com/2076-3417/12/18/9397 |
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