MOTIA2: Technical report

MoTIA2 is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (ERDF). The main objective of this project is to propose, investigate and implement a series of Industry 4.0 techniques to be applied in the mass breeding of insects in biofactories. In particular, the focus will be on three general objectives: (a) implement high-speed classification and counting algorithms applied to the counting and classification of tiger mosquito pupae and larvae; (b) develop AI-based computer audition algorithms for the detection of sound anomalies and apply them to the detection of females in Sterile Insect Technique (SIT) release containers; (c) develop and implement AI-based computer vision algorithms for the detection and classification of mosquito eggs in still images of deployed field traps.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-02
DOI
https://doi.org/10.5281/zenodo.14258770
Primary Topic
Digital Imaging for Blood Diseases
Type
article
Field-Weighted Citation Impact
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article

MOTIA2: Technical report

Zenodo (CERN European Organization for Nuclear Research)
Digital Imaging for Blood Diseases
article

MOTIA2: Technical report

article en

Abstract

MoTIA2 is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (ERDF). The main objective of this project is to propose, investigate and implement a series of Industry 4.0 techniques to be applied in the mass breeding of insects in biofactories. In particular, the focus will be on three general objectives: (a) implement high-speed classification and counting algorithms applied to the counting and classification of tiger mosquito pupae and larvae; (b) develop AI-based computer audition algorithms for the detection of sound anomalies and apply them to the detection of females in Sterile Insect Technique (SIT) release containers; (c) develop and implement AI-based computer vision algorithms for the detection and classification of mosquito eggs in still images of deployed field traps.

Zenodo (CERN European Organization for Nuclear Research)
Fundación Centro Tecnológico de la Información y la Comunicación (ES)
Industry, innovation and infrastructure, Partnerships for the goals
Openalex Percentile: Top 67%
Digital Imaging for Blood Diseases
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