Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning
We describe the development and validation of an autonomous monitoring station that identifies and records the movement of social insects into and out of the colony. The hardware consists of an illuminated channel and a fixed camera to capture the wasps' activities. An ad hoc post-processing softwar...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2024
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| Acceso en línea: | http://hdl.handle.net/20.500.12123/18525 https://resjournals.onlinelibrary.wiley.com/doi/10.1111/afe.12638 https://doi.org/10.1111/afe.12638 |
| _version_ | 1855486189940768768 |
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| author | Martinez Von Ellrich, Andres Dreidemie, Carola Inchaurza, Fernan Cucurull, Agustin Basti, Marian Masciocchi, Maite |
| author_browse | Basti, Marian Cucurull, Agustin Dreidemie, Carola Inchaurza, Fernan Martinez Von Ellrich, Andres Masciocchi, Maite |
| author_facet | Martinez Von Ellrich, Andres Dreidemie, Carola Inchaurza, Fernan Cucurull, Agustin Basti, Marian Masciocchi, Maite |
| author_sort | Martinez Von Ellrich, Andres |
| collection | INTA Digital |
| description | We describe the development and validation of an autonomous monitoring station that identifies and records the movement of social insects into and out of the colony. The hardware consists of an illuminated channel and a fixed camera to capture the wasps' activities. An ad hoc post-processing software was developed to identify the direction of movement and caste of the recorded individuals.
Validation results indicate that the model can detect with high levels of accuracy the presence of workers, drones and gynes, whereas direction of movement is accurate only for workers and drones, but not for gynes. Further development of the software and hardware should enable higher levels of accuracy, especially in terms of the direction of movement of reproductive individuals. This innovative tool holds immense potential for advancing ecological and behavioural research by providing researchers with rapid and easily accessible data. Understanding the activity patterns of individual wasps within the colony can yield valuable insights into factors influencing their growth, foraging patterns and the behaviour of reproductive individuals. Ultimately, this information can be incorporated into effective management plans for controlling harmful social insect populations in both ecological and productive systems. |
| format | Artículo |
| id | INTA18525 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | INTA185252024-07-16T12:23:58Z Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning Martinez Von Ellrich, Andres Dreidemie, Carola Inchaurza, Fernan Cucurull, Agustin Basti, Marian Masciocchi, Maite Vespidae Plant Pests Nesting Monitoring Research Vespula Big Data Neural Networks Plagas de Plantas Nidificación Vigilancia Investigación Macrodato Red de Neuronas Avipa Chaqueta Amarilla Yellowjacket We describe the development and validation of an autonomous monitoring station that identifies and records the movement of social insects into and out of the colony. The hardware consists of an illuminated channel and a fixed camera to capture the wasps' activities. An ad hoc post-processing software was developed to identify the direction of movement and caste of the recorded individuals. Validation results indicate that the model can detect with high levels of accuracy the presence of workers, drones and gynes, whereas direction of movement is accurate only for workers and drones, but not for gynes. Further development of the software and hardware should enable higher levels of accuracy, especially in terms of the direction of movement of reproductive individuals. This innovative tool holds immense potential for advancing ecological and behavioural research by providing researchers with rapid and easily accessible data. Understanding the activity patterns of individual wasps within the colony can yield valuable insights into factors influencing their growth, foraging patterns and the behaviour of reproductive individuals. Ultimately, this information can be incorporated into effective management plans for controlling harmful social insect populations in both ecological and productive systems. EEA Bariloche Fil: Martinez, Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina Fil: Martinez, Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina Fil: Dreidemie, Carola. Universidad Nacional de Rio Negro. Centro Interdisciplinario de Telecomunicaciones, Electrónica, Computación y Ciencias Aplicadas. Laboratorio de Visualización y Código Creativo; Argentina Fil: Dreidemie, Carola. La Rochelle Université. Policémies; Francia Fil: Inchaurza, Fernan. Universidad Nacional de Rio Negro. Centro Interdisciplinario de Telecomunicaciones, Electrónica, Computación y Ciencias Aplicadas. Laboratorio de Visualización y Código Creativo; Argentina Fil: Cucurull, Agustin. Universidad Nacional de Rio Negro. Centro Interdisciplinario de Telecomunicaciones, Electrónica, Computación y Ciencias Aplicadas. Laboratorio de Visualización y Código Creativo; Argentina Fil: Basti, Marian. Universidad Nacional de Rio Negro. Centro Interdisciplinario de Telecomunicaciones, Electrónica, Computación y Ciencias Aplicadas. Laboratorio de Visualización y Código Creativo; Argentina Fil: Masciocchi, Maite. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina Fil: Masciocchi, Maite. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina 2024-07-16T12:18:51Z 2024-07-16T12:18:51Z 2024-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/18525 https://resjournals.onlinelibrary.wiley.com/doi/10.1111/afe.12638 1461-9555 1461-9563 https://doi.org/10.1111/afe.12638 eng info:eu-repograntAgreement/INTA/2023-PE-L03-I033, Gestión Sostenible de los sistemas forestales naturales y cultivados para el desarrollo de los territorios y la provisión de servicios ecosistémicos en Patagonia Andina info:eu-repo/semantics/restrictedAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Wiley Agricultural and Forest Entomology : 1-13 (First published: 05 July 2024) |
| spellingShingle | Vespidae Plant Pests Nesting Monitoring Research Vespula Big Data Neural Networks Plagas de Plantas Nidificación Vigilancia Investigación Macrodato Red de Neuronas Avipa Chaqueta Amarilla Yellowjacket Martinez Von Ellrich, Andres Dreidemie, Carola Inchaurza, Fernan Cucurull, Agustin Basti, Marian Masciocchi, Maite Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning |
| title | Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning |
| title_full | Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning |
| title_fullStr | Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning |
| title_full_unstemmed | Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning |
| title_short | Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning |
| title_sort | advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning |
| topic | Vespidae Plant Pests Nesting Monitoring Research Vespula Big Data Neural Networks Plagas de Plantas Nidificación Vigilancia Investigación Macrodato Red de Neuronas Avipa Chaqueta Amarilla Yellowjacket |
| url | http://hdl.handle.net/20.500.12123/18525 https://resjournals.onlinelibrary.wiley.com/doi/10.1111/afe.12638 https://doi.org/10.1111/afe.12638 |
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