Utilizing Computer Vision and Deep Learning to Detect and Monitor Insects in Real Time by Analyzing Camera Trap Images

Year 2024, Volume: 9 Issue: 2, 280 - 292, 30.10.2024

Debarghya Biswas , Ankita Tiwari

https://doi.org/10.28978/nesciences.1575480

Abstract

Insect monitoring techniques are often labor-intensive and need significant resources for identifying species after manual field traps. Insect traps are usually maintained every week, leading to a low temporal accuracy of information collected that impedes ecological analysis. This study introduces a handheld computer vision device to attract and detect real insects. The research explicitly proposes identifying and categorizing species by imaging live species drawn to a camera trapping. An Automatic Moth Trapping (AMT) equipped with light elemnets and a camera was developed to draw and observe insects throughout twilight and nocturnal periods. Moth Classification and Counting (MCC) utilizes Computer Vision (CV) and Deep Learning (DL) evaluation of collected pictures and monitors. It enumerates insect populations while identifying moth species. Over 48 nights, more than 250k photos were captured, averaging 5.6k daily. A tailored Convolutional Neural Networks (CNN) was developed on 2000 labeled photos of live insects across eight distinct categories. The suggested computer vision method and methodology have shown encouraging outcomes as an economical option for automated surveillance of insects.

Keywords

Insect, deep learning, computer vision, camera trap images

References

• Amarathunga, D. C., Grundy, J., Parry, H., & Dorin, A. (2021). Methods of insect image capture and classification: A systematic literature review. Smart Agricultural Technology, 1, 100023. https://doi.org/10.1016/j.atech.2021.100023
• Choi, J., & Zhang, X. (2022). Classifications of restricted web streaming contents based on convolutional neural network and long short-term memory (CNN-LSTM). Journal of Internet Services and Information Security, 12(3), 49-62.
• Crossley, M. S., Meier, A. R., Baldwin, E. M., Berry, L. L., Crenshaw, L. C., Hartman, G. L., & Moran, M. D. (2020). No net insect abundance and diversity declines across US Long Term Ecological Research sites. Nature Ecology & Evolution, 4(10), 1368-1376.
• Flórián, N., Jósvai, J. K., Tóth, Z., Gergócs, V., Sipőcz, L., Tóth, M., & Dombos, M. (2023). Automatic detection of moths (Lepidoptera) with a funnel trap prototype. Insects, 14(4), 381. https://doi.org/10.3390/insects14040381
• Hereward, H. F., Facey, R. J., Sargent, A. J., Roda, S., Couldwell, M. L., Renshaw, E. L., & Thomas, R. J. (2021). Raspberry Pi nest cameras: An affordable tool for remote behavioral and conservation monitoring of bird nests. Ecology and Evolution, 11(21), 14585-14597.
• Hosseinzadeh, M., Rahmani, A. M., Husari, F. M., Alsalami, O. M., Marzougui, M., Nguyen, G. N., & Lee, S. W. (2024). A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing. Archives of Computational Methods in Engineering, 1-42.
• Iman, M.B., Qusay, A.A., Inass, S.H., & Refed, A.J. (2023). Mobile-computer Vision Model with Deep Learning for Testing Classification and Status of Flowers Images by using IoTs Devices. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 14(1), 82-94.
• Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing, 173, 24-49.
• Lello, F., Dida, M., Mkiramweni, M., Matiko, J., Akol, R., Nsabagwa, M., & Katumba, A. (2023). Fruit fly automatic detection and monitoring techniques: A review. Smart Agricultural Technology, 100294. https://doi.org/10.1016/j.atech.2023.100294
• Li, W., Zheng, T., Yang, Z., Li, M., Sun, C., & Yang, X. (2021). Classification and detection of insects from field images using deep learning for smart pest management: A systematic review. Ecological Informatics, 66, 101460. https://doi.org/10.1016/j.ecoinf.2021.101460
• Llopiz-Guerra, K., Daline, U.R., Ronald, M.H., Valia, L.V.M., Jadira, D.R.J.N., & Karla, R.S. (2024). Importance of Environmental Education in the Context of Natural Sustainability. Natural and Engineering Sciences, 9(1), 57-71.
• Mutsaerts, H. J., Petr, J., Groot, P., Vandemaele, P., Ingala, S., Robertson, A. D., & Barkhof, F. (2020). ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies. Neuroimage, 219, 117031. https://doi.org/10.1016/j.neuroimage.2020.117031
• Naqvi, Q., Wolff, P. J., Molano‐Flores, B., & Sperry, J. H. (2022). Camera traps are an effective tool for monitoring insect–plant interactions. Ecology and Evolution, 12(6), e8962. https://doi.org/10.1002/ece3.8962
• Roosjen, P. P., Kellenberger, B., Kooistra, L., Green, D. R., & Fahrentrapp, J. (2020). Deep learning for automated detection of Drosophila suzukii: potential for UAV‐based monitoring. Pest Management Science, 76(9), 2994-3002.
• Stanković, M., & Ćurĉić, M. (2020). New Species in the Arachnofauna of Bosnia and Herzegovina from the Protected Habitat of Gromiţelj, Velino Selo. Archives for Technical Sciences, 1(22), 67–78.
• Surendar, A., Saravanakumar, V., Sindhu, S., & Arvinth, N. (2024). A Bibliometric Study of Publication-Citations in a Range of Journal Articles. Indian Journal of Information Sources and Services, 14(2), 97-103. https://doi.org/10.51983/ijiss-2024.14.2.14
• Theivaprakasham, H. (2021). Identification of Indian butterflies using deep convolutional neural network. Journal of Asia-Pacific Entomology, 24(1), 329-340.
• Wilson, R. J., de Siqueira, A. F., Brooks, S. J., Price, B. W., Simon, L. M., Van Der Walt, S. J., & Fenberg, P. B. (2023). Applying computer vision to digitised natural history collections for climate change research: Temperature‐size responses in British butterflies. Methods in Ecology and Evolution, 14(2), 372-384.
• Xin, D., Chen, Y. W., & Li, J. (2020). Fine-grained butterfly classification in ecological images using squeeze-and-excitation and spatial attention modules. Applied Sciences, 10(5), 1681. https://doi.org/10.3390/app10051681