Advancing Social Insect research through the Development of an Automated Yellowjacket Nest-Activity Monitoring Station using Deep Learning

Fil: Martinez Von Ellrichshausen, Andrés Santiago. Grupo de Ecología de Poblaciones de Insectos, IFAB, Instituto de Investigaciones Forestales y Agropecuarias Bariloche, INTA - CONICET. Argentina.

Detalles Bibliográficos
Autores principales: Martinez Von Ellrichshausen, Andrés Santiago, Dreidemie, Carola, Inchaurza, Fernan, Cucurull, Agustín Julian, Basti, Mariano, Masciocchi, Maite
Lenguaje:Inglés
Publicado: Royal Entomology Society 2024
Materias:
Ciencias Agrarias
Humanidades
Ingeniería, Ciencia y Tecnología
Automatic caste recognition
Automation
Big data
Machine learning
Neural network
Pest
Social insects
Acceso en línea:https://resjournals.onlinelibrary.wiley.com/doi/10.1111/afe.12638
http://rid.unrn.edu.ar/handle/20.500.12049/12185
http://hdl.handle.net/20.500.12123/18525
Aporte de:
Repositorio Institucional Digital de la Universidad Nacional de Río Negro (UNRN) de Universidad Nacional de Río Negro
id I65-R171-20.500.12049-12185
record_format dspace
institution Universidad Nacional de Río Negro
institution_str I-65
repository_str R-171
collection Repositorio Institucional Digital de la Universidad Nacional de Río Negro (UNRN)
language Inglés
orig_language_str_mv en
topic Ciencias Agrarias
Humanidades
Ingeniería, Ciencia y Tecnología
Automatic caste recognition
Automation
Big data
Machine learning
Neural network
Pest
Social insects
Ciencias Agrarias
Humanidades
Ingeniería, Ciencia y Tecnología
spellingShingle Ciencias Agrarias
Humanidades
Ingeniería, Ciencia y Tecnología
Automatic caste recognition
Automation
Big data
Machine learning
Neural network
Pest
Social insects
Ciencias Agrarias
Humanidades
Ingeniería, Ciencia y Tecnología
Martinez Von Ellrichshausen, Andrés Santiago
Dreidemie, Carola
Inchaurza, Fernan
Cucurull, Agustín Julian
Basti, Mariano
Masciocchi, Maite
Advancing Social Insect research through the Development of an Automated Yellowjacket Nest-Activity Monitoring Station using Deep Learning
topic_facet Ciencias Agrarias
Humanidades
Ingeniería, Ciencia y Tecnología
Automatic caste recognition
Automation
Big data
Machine learning
Neural network
Pest
Social insects
Ciencias Agrarias
Humanidades
Ingeniería, Ciencia y Tecnología
description Fil: Martinez Von Ellrichshausen, Andrés Santiago. Grupo de Ecología de Poblaciones de Insectos, IFAB, Instituto de Investigaciones Forestales y Agropecuarias Bariloche, INTA - CONICET. Argentina.
author Martinez Von Ellrichshausen, Andrés Santiago
Dreidemie, Carola
Inchaurza, Fernan
Cucurull, Agustín Julian
Basti, Mariano
Masciocchi, Maite
author_facet Martinez Von Ellrichshausen, Andrés Santiago
Dreidemie, Carola
Inchaurza, Fernan
Cucurull, Agustín Julian
Basti, Mariano
Masciocchi, Maite
author_sort Martinez Von Ellrichshausen, Andrés Santiago
title 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_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_sort advancing social insect research through the development of an automated yellowjacket nest-activity monitoring station using deep learning
publisher Royal Entomology Society
publishDate 2024
url https://resjournals.onlinelibrary.wiley.com/doi/10.1111/afe.12638
http://rid.unrn.edu.ar/handle/20.500.12049/12185
http://hdl.handle.net/20.500.12123/18525
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AT inchaurzafernan advancingsocialinsectresearchthroughthedevelopmentofanautomatedyellowjacketnestactivitymonitoringstationusingdeeplearning
AT cucurullagustinjulian advancingsocialinsectresearchthroughthedevelopmentofanautomatedyellowjacketnestactivitymonitoringstationusingdeeplearning
AT bastimariano advancingsocialinsectresearchthroughthedevelopmentofanautomatedyellowjacketnestactivitymonitoringstationusingdeeplearning
AT masciocchimaite advancingsocialinsectresearchthroughthedevelopmentofanautomatedyellowjacketnestactivitymonitoringstationusingdeeplearning
AT martinezvonellrichshausenandressantiago automatedsocialwasptrafficmonitoringstation
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spelling I65-R171-20.500.12049-121852024-11-07T13:21:15Z application/pdf info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/4.0/ 2024-07-05 Fil: Martinez Von Ellrichshausen, Andrés Santiago. Grupo de Ecología de Poblaciones de Insectos, IFAB, Instituto de Investigaciones Forestales y Agropecuarias Bariloche, INTA - CONICET. Argentina. Fil: Dreidemie, Carola. LVCC Laboratorio de Investigacion y Desarrollo en Tecnologías de Visualización, Computación Gráfica y Código Creativo, Universidad Nacional de Rio Negro. Río Negro; Argentina. Fil: Inchaurza, Fernan. LVCC Laboratorio de Investigacion y Desarrollo en Tecnologías de Visualización, Computación Gráfica y Código Creativo, Universidad Nacional de Rio Negro. Río Negro; Argentina. Fil: Cucurull, Agustín Julian. Universidad Nacional de Rio Negro. Río Negro; Argentina. Fil: Basti, Mariano. Universidad Nacional de Rio Negro. Río Negro; Argentina. Fil: Masciocchi, Maite. Grupo de Ecología de Poblaciones de Insectos, IFAB, Instituto de Investigaciones Forestales y Agropecuarias Bariloche, INTA - CONICET. Río Negro; Argentina. Advancing Social Insect research through the Development of an Automated Yellowjacket Nest-Activity Monitoring Station using Deep Learning Automated Social Wasp Traffic Monitoring Station Martinez Von Ellrichshausen, Andrés Santiago Dreidemie, Carola Inchaurza, Fernan Cucurull, Agustín Julian Basti, Mariano Masciocchi, Maite Ciencias Agrarias Humanidades Ingeniería, Ciencia y Tecnología Automatic caste recognition Automation Big data Machine learning Neural network Pest Social insects Ciencias Agrarias Humanidades Ingeniería, Ciencia y Tecnología 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 is robust in recognising direction of movement of the wasps and identifying caste. 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. true The development a monitoring tool to facilitate detailed studies of incoming and outgoing individuals of social insect colonies. We designed hardware that can be positioned at the entrance of wasp nests, which is equipped with a camera and integrated with automated recognition capabilities, records the movement (entry or exit) of each individual in the colony and then identifies the caste of the individual (worker, drone, or gyne) and the direction of movement (inward or outward respective of the nest). The development of this equipment involved creating a support structure to record wasp movement throughout the season. We also developed post-processing software, trained through deep learning, intended to detect worker, drones, and gyne individual movements, under the assumption that morphological differences between castes could be used to identify them. Martínez, A.S.; Dreidemie, C; Inchaurza, F.; Cucurull, A.; Basti, M.; Masciochi, M.. Advancing Social Insect Research through the Development of an Automated Yellowjacket Nest-Activity Monitoring Station using Deep Learning. Special Issue: Advances in Insect Biomonitoring for Agriculture and Forestry. Ed.: Jordan Cuff. Royal Entomological Society, UK 1461-9555 1461-9563 https://resjournals.onlinelibrary.wiley.com/doi/10.1111/afe.12638 http://rid.unrn.edu.ar/handle/20.500.12049/12185 http://hdl.handle.net/20.500.12123/18525 en https://resjournals.onlinelibrary.wiley.com/doi/10.1111/afe.12638 Special Issue "Advances in Insect Biomonitoring for Agriculture and Forestry"- Ed: Jordan Cuff Agricultural and Forest Entomology Royal Entomology Society

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