Applying Computer Vision Models to Detect in Real Time the Pollen Flow at the Input of Honeybee Hives (Apis mellifera L.)
Resumo
Pollen flow into the beehives is directly related to the strength and the health of the honeybee (Apis mellifera L.) colonies. Traditional monitoring of beehives is done through manual and invasive inspections, which are time-consuming, stress bees. On the other hand, the recent paradigm of precision beekeeping has allowed for remote and non-invasive monitoring of hives. In this study, six computer vision models were applied to videos recorded at the entrance of honeybee hives to detect incoming pollen flow. The results showed that the YOLOv7 model performed better in the FPS metric, while also achieving a metric of AP75 superior to 77%. The CenterNet model presented the best metrics for real-time applications, with an excellent predictive performance at low computational cost.
Referências
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Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159.
Zou, Z., Chen, K., Shi, Z., Guo, Y., and Ye, J. (2023). Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3):257–276.
Babic, Z., Pilipovic, R., Risojevic, V., and Mirjanic, G. (2016). Pollen bearing honey bee detection in hive entrance video recorded by remote embedded system for pollination monitoring. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7:51–57.
Brodschneider, R., Kalcher-Sommersguter, E., Kuchling, S., Dietemann, V., Gray, A., Božič, J., Briedis, A., Carreck, N. L., Chlebo, R., Crailsheim, K., et al. (2021). Csi pollen: diversity of honey bee collected pollen studied by citizen scientists. Insects, 12(11).
Cai, Z. and Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Couto, R. H. N. and Couto, L. A. (2006). Apicultura: manejo e produtos. Funep Jaboticabal.
Fewell, J. H. (2003). Social insect networks. Science, 301(5641):1867–1870.
Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430.
Hasler, D. and Suesstrunk, S. E. (2003). Measuring colorfulness in natural images. In Rogowitz, B. E. and Pappas, T. N., editors, Human Vision and Electronic Imaging VIII, volume 5007, pages 87–95. International Society for Optics and Photonics, SPIE.
Kale, D. J., Tashakkori, R., and Parry, R. M. (2015). Automated beehive surveillance using computer vision. In SoutheastCon 2015, pages 1–3.
Khalifa, S. A., Elshafiey, E. H., Shetaia, A. A., El-Wahed, A. A. A., Algethami, A. F., Musharraf, S. G., AlAjmi, M. F., Zhao, C., Masry, S. H., Abdel-Daim, M. M., et al. (2021). Overview of bee pollination and its economic value for crop production. Insects, 12(8).
Moulden, B., Kingdom, F., and Gatley, L. F. (1990). The standard deviation of luminance as a metric for contrast in random-dot images. Perception, 19(1):79–101. PMID: 2336338.
Ngo, T. N., Rustia, D. J. A., Yang, E.-C., and Lin, T.-T. (2021). Automated monitoring and analyses of honey bee pollen foraging behavior using a deep learning-based imaging system. Computers and Electronics in Agriculture, 187:106239.
Nicholls, E. and Hempel de Ibarra, N. (2017). Assessment of pollen rewards by foraging bees. Functional Ecology, 31(1):76–87.
Ollerton, J., Winfree, R., and Tarrant, S. (2011). How many flowering plants are pollinated by animals? Oikos, 120(3):321–326.
O’Neill, M. E. and Mathews, K. (2000). Theory & methods: A weighted least squares approach to levene’s test of homogeneity of variance. Australian & New Zealand Journal of Statistics, 42(1):81–100.
Ren, S., He, K., Girshick, R., and Sun, J. (2017). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137–1149.
Seeley, T. (2006). Ecologia da Abelha: um estudo de adaptação na vida social (tradução de CA Osowski). LTDA, Porto Alegre.
Shapiro, S. S. and Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4):591–611.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Hook, NY, USA. Curran Associates Inc.
Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y. M. (2023). Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7464–7475.
Yang, C. and Collins, J. (2019). Deep learning for pollen sac detection and measurement on honeybee monitoring video. In 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), pages 1–6.
Zhou, X., Wang, D., and Krähenbühl, P. (2019). Objects as points. arXiv preprint arXiv:1904.07850.
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159.
Zou, Z., Chen, K., Shi, Z., Guo, Y., and Ye, J. (2023). Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3):257–276.
Publicado
06/08/2023
Como Citar
SILVA, Daniel de Amaral da; BOMFIM, Isac Gabriel Abrahão; BRAGA, Antonio Rafael; GOMES, Danielo G..
Applying Computer Vision Models to Detect in Real Time the Pollen Flow at the Input of Honeybee Hives (Apis mellifera L.). In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 14. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 21-30.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2023.230588.
