Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection
Rapid detection and identification of the extent of spruce bark beetle infestation in the field is a necessity for researchers, forest rangers, and tree protection agencies. This paper presents an innovative solution that uses an autonomous multifunctional probe with embedded artificial intelligence...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098624000235 |
| _version_ | 1797259952044113920 |
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| author | Milan Novak Petr Doležal Ondřej Budík Ladislav Ptáček Jakub Geyer Markéta Davídková Miloš Prokýšek |
| author_facet | Milan Novak Petr Doležal Ondřej Budík Ladislav Ptáček Jakub Geyer Markéta Davídková Miloš Prokýšek |
| author_sort | Milan Novak |
| collection | DOAJ |
| description | Rapid detection and identification of the extent of spruce bark beetle infestation in the field is a necessity for researchers, forest rangers, and tree protection agencies. This paper presents an innovative solution that uses an autonomous multifunctional probe with embedded artificial intelligence. This approach is based on the real-time object detection method, called “first object, more object” (FOMO), which is adapted to the energy-efficient architecture of the ESP32 microcontroller. Neural network algorithms for fast image processing were trained using data sets from various locations in central Europe, which were heavily affected by the bark beetle calamity. The results of the experimental verification of the deployment of this smart probe in the field demonstrate a high level of precision in the detection and identification of the extent of damage. |
| first_indexed | 2024-03-08T03:31:30Z |
| format | Article |
| id | doaj.art-a55c54d623214eb191da7289bb248bbb |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-04-24T23:17:36Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-a55c54d623214eb191da7289bb248bbb2024-03-17T07:54:29ZengElsevierEngineering Science and Technology, an International Journal2215-09862024-03-0151101637Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detectionMilan Novak0Petr Doležal1Ondřej Budík2Ladislav Ptáček3Jakub Geyer4Markéta Davídková5Miloš Prokýšek6Faculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 1760, Ceske Budejovice, CZ-37005, Czechia; Corresponding author.Faculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 1760, Ceske Budejovice, CZ-37005, Czechia; Institute of Entomology, Biology Centre, Czech Academy of Sciences, Branisovska 31, Ceske Budejovice, CZ-37005, CzechiaFaculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 1760, Ceske Budejovice, CZ-37005, CzechiaFaculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 1760, Ceske Budejovice, CZ-37005, CzechiaFaculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 1760, Ceske Budejovice, CZ-37005, CzechiaInstitute of Entomology, Biology Centre, Czech Academy of Sciences, Branisovska 31, Ceske Budejovice, CZ-37005, CzechiaFaculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 1760, Ceske Budejovice, CZ-37005, CzechiaRapid detection and identification of the extent of spruce bark beetle infestation in the field is a necessity for researchers, forest rangers, and tree protection agencies. This paper presents an innovative solution that uses an autonomous multifunctional probe with embedded artificial intelligence. This approach is based on the real-time object detection method, called “first object, more object” (FOMO), which is adapted to the energy-efficient architecture of the ESP32 microcontroller. Neural network algorithms for fast image processing were trained using data sets from various locations in central Europe, which were heavily affected by the bark beetle calamity. The results of the experimental verification of the deployment of this smart probe in the field demonstrate a high level of precision in the detection and identification of the extent of damage.http://www.sciencedirect.com/science/article/pii/S2215098624000235Bark beetleSpruceTreeIoTProtectionNeural networks |
| spellingShingle | Milan Novak Petr Doležal Ondřej Budík Ladislav Ptáček Jakub Geyer Markéta Davídková Miloš Prokýšek Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection Engineering Science and Technology, an International Journal Bark beetle Spruce Tree IoT Protection Neural networks |
| title | Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection |
| title_full | Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection |
| title_fullStr | Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection |
| title_full_unstemmed | Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection |
| title_short | Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection |
| title_sort | intelligent inspection probe for monitoring bark beetle activities using embedded iot real time object detection |
| topic | Bark beetle Spruce Tree IoT Protection Neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2215098624000235 |
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