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  • Development and benchmarking of sensing technologies for Tenebrio molitor dead pupae identification
    Publication . Oliveira, Francisco; Tinoco, Vítor; Rodrigues, Leandro; Santos, Filipe N.; Cunha, Mário; Vieira, Inês; Santos, Marisa V.
    The production of insects for both human and animal consumption has seen an interest increase in recent years. Tenebrio molitor, a beetle species whose larval stage was considered by the European Commission safe for human consumption, represents a promising candidate for large-scale production. Efficient production of T. molitor requires the separation of its different metamorphic stages to optimize productivity and reduce cannibalism. Additionally, dead pupae must be removed to prevent contamination and ensure the healthy development of the remaining individuals. Currently, the identification of dead pupae relies primarily on visual inspection based on colour changes, as dead individuals tend to exhibit surface melanization. However, this method becomes inefficient in large-scale operations. This study presents a benchmark of hyperspectral imaging (HSI) and thermal imaging as alternative sensing technologies for automated dead pupae identification. Hyperspectral data acquired in the near-infrared range (900–1700 nm) enabled accurate discrimination between dead and live pupae using both a PLS-DA and a Logistic Regression model, achieving F1-Scores above 90 % for both classes. Furthermore, key wavelengths (903 nm and 1259 nm) were identified and used to develop a normalized difference index (NDI) capable of distinguishing pupae health status using only two spectral bands. Thermal imaging revealed consistent temperature differences, with dead pupae presenting approximately 1–3 % lower temperatures than live pupae. The proven reliability and precision of the proposed sensing technologies validate their use in contributing to the development of scalable and reliable solutions for quality control in the insect farming industry.
    2026-06Contributo em revista Acesso aberto Ver mais