Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta)

TL;DR

This paper introduces a low-cost multisensor system utilizing AI-based data fusion for insect classification, aiming to enhance biodiversity monitoring and conservation efforts.

Contribution

It presents a novel multisensor setup combining visual, wing beat, and environmental data for insect classification, tested in laboratory and field conditions.

Findings

Promising initial classification results on a small unbalanced dataset.

System effectively integrates multiple sensors for insect identification.

Supports biodiversity and agricultural research applications.

Abstract

Insect populations are declining globally, making systematic monitoring essential for conservation. Most classical methods involve death traps and counter insect conservation. This paper presents a multisensor approach that uses AI-based data fusion for insect classification. The system is designed as low-cost setup and consists of a camera module and an optical wing beat sensor as well as environmental sensors to measure temperature, irradiance or daytime as prior information. The system has been tested in the laboratory and in the field. First tests on a small very unbalanced data set with 7 species show promising results for species classification. The multisensor system will support biodiversity and agriculture studies.