ApisFlow: a Real-Time Automated Tool to Detect, Classify and Count Honey Bees Castes at the Hive Entrance

• Gabriel Vasconcelos Fruet UFC
• Isac Gabriel Abrahão Bomfim IFCE
• Rafael Capelo Domingues UFC
• Antonio Rafael Braga UFC
• Danielo G. Gomes UFC

DOI: https://doi.org/10.5753/wcama.2023.230583

Resumo

There are three types of castes in honey bee (Apis mellifera L.) colony: the queen, workers, and drones. Although they are all important to perpetuate the species, drones do not collaborate with tasks in the colony and their counting may overestimate the real number of foraging workers, individuals who really contribute to pollination services. So, monitoring, classifying, and counting the flow and proportion of workers and drones through the beehive entrance provide useful information related to the colony’s well-being. This has become possible thanks to the so-called Precision Beekeeeping, an emerging field of digital agriculture to gather and transfer bee-related data over time. Here, we propose ApisFlow, a real-time object-tracking framework for automatically detecting, tracking, classifying and counting the flow of honey bee castes at the hive entrance. ApisFlow uses computer vision and machine learning methods and algorithms. We strongly believe that ApisFlow allows bee counting, tracking, and classification in a less laborious, safe, fast, and accurate way to help beekeepers in making decisions saving time. Suggesting a high-precision algorithm with a mean error rate below 5%.

Referências

Albuquerque, D. Q., Braga, A. R., Bomfim, I. G. A., and Gomes, D. G. (2022). Aplicando um modelo yolo para detectar e diferenciar por imagem castas de abelhas melíferas de forma automatizada. In Anais do XIII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, pages 51–60. SBC.

Andrijević, N., Urošević, V., Arsić, B., Herceg, D., and Savić, B. (2022). Iot monitoring and prediction modeling of honeybee activity with alarm. Electronics, 11(5):783.

Barros, C., Freitas, E. D., Braga, A. R., Bomfim, I. G., and Gomes, D. (2021). Aplicando redes neurais convolucionais em imagens para reconhecimento automatizado de abelhas melíferas (Apis mellifera l.). In Anais do XII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, pages 19–28, Porto Alegre, RS, Brasil. SBC. https://doi.org/10.5753/wcama.2021.15733.

Boes, K. (2010). Honeybee colony drone production and maintenance in accordance with environmental factors: an interplay of queen and worker decisions. Insectes sociaux, 57:1–9.

Delaplane, K. S., Dag, A., Danka, R. G., Freitas, B. M., Garibaldi, L. A., Goodwin, R. M., and Hormaza, J. I. (2013). Standard methods for pollination research with apis mellifera. Journal of Apicultural Research, 52(4):1–28.

Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation.

Hadjur, H., Ammar, D., and Lefèvre, L. (2022). Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services. Computers and Electronics in Agriculture, 192:106604. https://doi.org/10.1016/j.compag.2021.106604.

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications.

Hyndman, R. J. and Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4):679–688.

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):688.

Odemer, R. (2022). Approaches, challenges and recent advances in automated bee counting devices: A review. Annals of Applied Biology, 180(1):73–89.

O’Brien, W., Tashakkori, R., Parry, R. M., Hamza, A., and Graber, J. (2022). Estimating the number of drones at the entrance of a honey bee hive using machine learning tools. In SoutheastCon 2022, pages 397–404.

Sagili, R. R., Burgett, D., et al. (2011). Evaluating honey bee colonies for pollination: a guide for commercial growers and beekeepers.

Yang, F., Zhang, X., and Liu, B. (2022). Video object tracking based on yolov7 and deepsort. arXiv preprint arXiv:2207.12202.

Yin, Y., Li, H., and Fu, W. (2020). Faster-yolo: An accurate and faster object detection method. Digital Signal Processing, 102:102756.