DOI - Mendel University Press

DOI identifiers

DOI: 10.11118/978-80-7701-100-6-0263

MOŽNOSTI VYUŽITÍ AI A IOT V PRODUKCI JEDLÉHO HMYZU

APPLICATIONS OF AI AND IOT IN EDIBLE INSECT PRODUCTION

Šárka Nedomová1, Vlastimil Slaný2, Ondřej Šťastník3, Andrea Roztočilová1, Vladimír Palupa4, Jiří Plachý4, Adam Kovál1, Barbora Odehnalová1, Vojtěch Kumbár5
1 Ústav technologie potravin, Agronomická fakulta, Mendelova univerzita v Brně, Zemědělská 1665/1, 613 00 Brno, Česká republika
2 Ústav zemědělské, potravinářské a environmentální techniky, Agronomická fakulta, Mendelova univerzita v Brně, Zemědělská 1665/1, 613 00 Brno, Česká republika
3 Ústav výživy zvířat a pícninářství, Agronomická fakulta, Mendelova univerzita v Brně, Zemědělská 1665/1, 613 00 Brno, Česká republika
4 Tecpa s.r.o., Tyršova 1132, 664 42 Modřice, Česká republika
5 Ústav techniky a automobilové dopravy, Agronomická fakulta, Mendelova univerzita v Brně, Zemědělská 1665/1, 613 00 Brno, Česká republika


Produkce jedlého hmyzu představuje dynamicky se rozvíjející odvětví zemědělství reagující na rostoucí globální poptávku po udržitelných zdrojích proteinů pro krmivářské i potravinářské využití. S objemem produkce vznikají požadavky na standardizaci kvality, welfare a ekonomickou efektivitu, přičemž roste význam technologických nástrojů umožňujících automatizaci a digitalizaci chovných procesů (např. pomocí technologie Internetu věcí a algoritmů umělé inteligence). Cílem příspěvku je popsat současné možnosti využití IoT a AI v produkci jedlého hmyzu a technologické přístupy k senzorickému monitoringu chovného prostředí, sledování biologických parametrů produkce a automatizovanému řízení mikroklimatických podmínek, dále využití algoritmů umělé inteligence v oblasti obrazové analýzy a predikci produkčních ukazatelů ve vztahu ke kvalitě výsledných produktů.

Keywords: edible insects, artificial intelligence, quality, production automation

pages: 263-269, online: 2026



References

  1. Agarwal, M., Al-Shuwaili, T., Nugaliyadde, A., Wang, P., Wong, K. W., Ren, Y. (2020): Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning. Computers and Electronics in Agriculture, 173, 105438. Dostupné z: https://doi.org/10.1016/j.compag.2020.105438 Go to original source...
  2. Baur, A., Koch, D., Gatternig, B., Delgado, A. (2022): Noninvasive monitoring system for Tenebrio molitor larvae based on image processing with a watershed algorithm and a neural net approach. Journal of Insects as Food and Feed, 8(8), 913-920. Dostupné z: https://doi.org/10.3920/JIFF2021.0185 Go to original source...
  3. Besson, M., Alison, J., Bjerge, K., Gorochowski, T. E., Høye, T. T., Jucker, T., Mann, H. M. R., Clements, C. F. (2022): Towards the fully automated monitoring of ecological communities. Ecology Letters, 25(12), 2753-2775. Dostupné z: https://doi.org/10.1111/ele.14123 Go to original source...
  4. Cruz-Tirado, J. P., dos Santos Vieira, M. S., Ferreira, R. S. B., Amigo, J. M., Batista, E. A. C., Barbin, D. F. (2025): Prediction of total lipids and fatty acids in black soldier fly (Hermetia illucens L.) dried larvae by NIR-hyperspectral imaging and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 329, 125646. Dostupné z: https://doi.org/10.1016/j.saa.2024.125646 Go to original source...
  5. García-Gutiérrez, N., Mellado-Carretero, J., Bengoa, C., Salvador, A., Sanz, T., Wang, J., Ferrando, M., Güell, C., de Lamo-Castellví, S. (2021): ATR-FTIR spectroscopy combined with multivariate analysis successfully discriminates raw doughs and baked 3D-printed snacks enriched with edible insect powder. Foods, 10(8), 1806. Dostupné z: https://doi.org/10.3390/foods10081806 Go to original source...
  6. Hansen, M. F., Oparaeke, A., Gallagher, R., Karimi, A., Tariq, F., Smith, M. L. (2022): Towards machine vision for insect welfare monitoring and behavioural insights. Frontiers in Veterinary Science, 9, 835529. Dostupné z: https://doi.org/10.3389/fvets.2022.835529 Go to original source...
  7. Hoque, A. (2024): Artificial intelligence in post-harvest drying technologies: A comprehensive review on optimization, quality enhancement, and energy efficiency. International Journal of Science and Research, 13, 493-502. Dostupné z: https://doi.org/10.21275/SR241107163717 Go to original source...
  8. Hou, Y., Zhao, P., Zhang, F., Yang, S., Rady, A., Wijewardane, N. K., Huang, J., Li, M. (2022): Fourier-transform infrared spectroscopy and machine learning to predict amino acid content of nine commercial insects. Food Science and Technology, 42, e100821. Dostupné z: https://doi.org/10.1590/fst.100821 Go to original source...
  9. Ibitoye, O., Ayeni, O., Ayanniyi, O., Wealth, A., Kolejo, O., Adenika, O. A., Murtala, M., Oyedijii, O., Aremu, A., Muritala, D. (2025): Advancing urban insect farming: Integrating automation, vertical farming, and sustainable waste management systems. Discover Agriculture, 3, 37. Dostupné z: https://doi.org/10.1007/s44279-025-00194-8 Go to original source...
  10. International Platform of Insects for Food and Feed. (2024, February). IPIFF guide on good hygiene practices for European Union (EU) producers of insects as food and feed. Dostupné z: https://ipiff.org/wp-content/uploads/2024/02/Folder-IPIFF_Guide_A4_19.02.2024_black-colour.pdf
  11. Kröncke, N., Baur, A., Böschen, V., Demtröder, S., Benning, R., Delgado, A. (2020): Automation of insect mass rearing and processing technologies of mealworms (Tenebrio molitor). In A. A. Mariod (Ed.), African edible insects as alternative source of food, oil, protein and bioactive components, 123-139. Springer. Dostupné z: https://doi.org/10.1007/978-3-030-32952-5_8 Go to original source...
  12. Majewski, P., Zapotoczny, P., Lampa, P., Burduk, R., Reiner, J. (2022): Multipurpose monitoring system for edible insect breeding based on machine learning. Scientific Reports, 12, 7892. Dostupné z: https://doi.org/10.1038/s41598-022-11794-5 Go to original source...
  13. Mellado-Carretero, J., García-Gutiérrez, N., Ferrando, M., Güell, C., García-Gonzalo, D., de Lamo-Castellví, S. (2020): Rapid discrimination and classification of edible insect powders using ATR-FTIR spectroscopy combined with multivariate analysis. Journal of Insects as Food and Feed, 6(2), 141-148. Dostupné z: https://doi.org/10.3920/JIFF2019.0032 Go to original source...
  14. Meyer-Rochow, V. B., Gahukar, R. T., Ghosh, S., Jung, C. (2021): Chemical composition, nutrient quality and acceptability of edible insects are affected by species, developmental stage, gender, diet, and processing method. Foods, 10(5), 1036. Dostupné z: https://doi.org/10.3390/foods10051036 Go to original source...
  15. Migliozzi, D., Cornaglia, M., Mouchiroud, L., Uhlmann, V., Unser, M. A., Auwerx, J., Gijs, M. A. M. (2018): Multimodal imaging and high-throughput image-processing for drug screening on living organisms on-chip. Journal of Biomedical Optics, 24(2), 021205. Dostupné z: https://doi.org/10.1117/1.JBO.24.2.021205 Go to original source...
  16. Nawoya, S., Geissmann, Q., Karstoft, H., Bjerge, K., Akol, R., Katumba, A., Mwikirize, C., Gebreyesus, G. (2025): Prediction of black soldier fly larval sex and morphological traits using computer vision and deep learning. Smart Agricultural Technology, 11, 100953. Dostupné z: https://doi.org/10.1016/j.atech.2025.100953 Go to original source...
  17. Ng, X. L., Ong, K. E., Zheng, Q., Ni, Y., Yeo, S. Y., Liu, J. (2022): Animal Kingdom: A large and diverse dataset for animal behavior understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 19023-19034. Dostupné z: https://doi.org/10.1109/CVPR52688.2022.01844 Go to original source...
  18. Ni, D., Nelis, J. L. D., Dawson, A. L., Bourne, N., Juliano, P., Colgrave, M. L., Juhász, A., Bose, U. (2024): Application of near-infrared spectroscopy and chemometrics for the rapid detection of insect protein adulteration from a simulated matrix. Food Control, 159, 110268. Dostupné z: https://doi.org/10.1016/j.foodcont.2023.110268 Go to original source...
  19. Papadopoulos, A.-M., Melissas, P., Kastellos, A., Katranitsiotis, P., Zaparas, P., Stavridis, K., Daras, P. (2024): TenebrioVision: A fully annotated dataset of Tenebrio molitor larvae worms in a controlled environment for accurate small object detection and segmentation. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, 187-196. Dostupné z: https://doi.org/10.5220/0012295900003654 Go to original source...
  20. Qu, J.-H., Liu, D., Cheng, J.-H., Sun, D.-W., Ma, J., Pu, H., Zeng, X.-A. (2015): Applications of near-infrared spectroscopy in food safety evaluation and control: A review of recent research advances. Critical Reviews in Food Science and Nutrition, 55(13), 1939-1954. Dostupné z: https://doi.org/10.1080/10408398.2013.871693 Go to original source...
  21. Romano, D. (2025): Novel automation, artificial intelligence, and biomimetic engineering advancements for insect studies and management. Current Opinion in Insect Science, 68, 101337. Dostupné z: https://doi.org/10.1016/j.cois.2025.101337 Go to original source...
  22. Sandstrom, D. J., Offord, B. W. (2022): Measurement of oxygen consumption in Tenebrio molitor using a sensitive, inexpensive, sensor-based coulometric microrespirometer. Journal of Experimental Biology, 225(9), jeb243966. Dostupné z: https://doi.org/10.1242/jeb.243966 Go to original source...
  23. Smith, M. J. (2019): Getting value from artificial intelligence in agriculture. Animal Production Science, 60(1), 46-54. Dostupné z: https://doi.org/10.1071/AN18522 Go to original source...
  24. Sumriddetchkajorn, S., Kamtongdee, C., Chanhorm, S. (2015): Fault-tolerant optical-penetration-based silkworm gender identification. Computers and Electronics in Agriculture, 119, 201-208. Dostupné z: https://doi.org/10.1016/j.compag.2015.10.004 Go to original source...
  25. Tao, D., Wang, Z., Li, G., Xie, L. (2019): Sex determination of silkworm pupae using VIS-NIR hyperspectral imaging combined with chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 208, 7-12. Dostupné z: https://doi.org/10.1016/j.saa.2018.09.049 Go to original source...