Detecção de Abelhas Nativas em Colmeias em Campo Utilizando Visão Computacional
Resumo
Abordagens de inteligência artificial, como visão computacional, podem ajudar a entender melhor o comportamento de abelhas e ajudar no seu manejo. Detectar espécies de abelhas em campo é ainda um desafio para os métodos tradicionais. Neste trabalho, propõe-se o uso do detector YOLO justificado por sua capacidade, velocidade e generalização. Foi possível detectar a grande maioria das abelhas presentes nos frames, com mAP de 99,5% de eficácia.
Palavras-chave:
Inteligência Artificial, Visão Computacional, Espécies De Abelhas
Referências
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Redmon, J. e Farhadi, A. (2016) “YOLO9000: Better, Faster, Stronger”, Cornell Univ., no. December. arXiv:1612.08242v1
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Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2015) “You Only Look Once: Unified, Real-Time Object Detection”, Cornell Univ., no. May. arXiv:1506.02640v5
Sánchez-bayo, F. e Wyckhuys, K. A. G. (2019) “Worldwide decline of the entomofauna: A review of its drivers”, Biol. Conserv., vol. 232, no. September, p. 8-27. doi: https://doi.org/10.1016/j.biocon.2019.01.020
Shen, Y., Zhou, H., Li, J., Jian, F. e Jayas, D. S. (2018) “Detection of stored-grain insects using deep learning”, Comput. Electron. Agric., vol. 145, no. October, p. 319-325. doi: https://doi.org/10.1016/j.compag.2017.11.039
Xia, D., Chen, P., Wang, B., Zhang, J. e Xie, C. (2018) “Insect Detection and Classification Based on an Improved Convolutional Neural Network”, Sensors, vol. 18, no. November, p. 1-12. doi: 10.3390/s18124169
Bochkovskiy, A., Wang, C. e Liao, H. M. (2020) “YOLOv4: Optimal Speed and Accuracy of Object Detection”, Cornell Univ., no. April. arXiv:2004.10934v1
Borges, R. C., Padovani, K., Imperatriz-Fonseca, V. L. e Giannini, T. C. (2020) “A dataset of multi-functional ecological traits of Brazilian bees”, Sci. Data, vol. 7, p. 1-9. doi: https://doi.org/10.1038/s41597-020-0461-3
Costa, L. (2019) Guia Fotográfico de Identificação de Abelhas Sem Ferrão, para resgate em áreas de supressão florestal. Belém: Brasil.
de Souza, P., Marendy, P., Barbosa, K., Budi, S., Hirsch, P., Nikolic, N., Gunthorpe, T. Pessin, G., Davie, A. (2018) “Low-Cost Electronic Tagging System for Bee Monitoring”, Sensors vol.18 2124. doi: 10.3390/s18072124
Filipiak, M. (2018) “A Better Understanding of Bee Nutritional Ecology Is Needed to Optimize Conservation Strategies for Wild Bees - The Application of Ecological Stoichiometry”, Insects, vol. 9, no. July, p. 1-13. doi: 10.3390/insects9030085
Giannini, T. C., Costa, W. F., Borges, R. C., Miranda L., Costa, C. P. W., Saraiva, A. M. e Fonseca, V. L. I. (2020) “Climate change in the Eastern Amazon: crop-pollinator and occurrence-restricted bees are potentially more affected”, Reg. Environ. Chang., vol. 20, p. 1-12. doi: https://doi.org/10.1007/s10113-020-01611-y
Gomes, P. A. B., Suhara, Y., Nunes-Silva, P., Costa, L., Arruda, H., Venturieri, G., Imperatriz-Fonseca, V. L., Pentland, A., Souza, P. e Pessin, G. (2020) “An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection”, Nature research, vol. 10, pp. 1-12. doi: https://doi.org/10.1038/s41598-019-56352-8
Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., Stenmans, W., Müller, A., Sumser, H., Hörren, T., Goulson, D. e de Kroon, H. (2017) “More than 75 percent decline over 27 years in total flying insect biomass in protected areas”, PLoS One, vol. 12, no. October, p. 18-22. doi: https://doi.org/10.1371/journal.pone.0185809
Júnior, T. D. C. and Rieder, R. (2020) “Automatic identification of insects from digital images: A survey”, Comput. Electron. Agric., vol. 178, no. April, p. 105784. doi: https://doi.org/10.1016/j.compag.2020.105784
Kuan, A. C., Grandi-Hoffman, G., Curry, R. J., Garber, K. V., Kanarek, A. R., Snyder, M. N., Wolfe, K. L. e Purucker, S. T. (2018) “Sensitivity analyses for simulating pesticide impacts on honey bee colonies”, Ecol. Modell., vol. 376, no. February, p. 15-27. doi: https://doi.org/10.1016/j.ecolmodel.2018.02.010
Liu, L., Wang, R., Xie, C., Yang, P., Wang, F., Surdiman, S. e Liu, W. (2019) “PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification”, IEEE Access, vol. 7, p. 45301-45312. doi: 10.1109/ACCESS.2019.2909522
Macharia, J. M., Gikungu, M. W., Karanja, R. e Okoth, S. (2020) “Managed bees as pollinators and vectors of bio control agent against grey mold disease in strawberry plantations”, African J. Agric. Res., vol. 16, no. 12, p. 1674-1680. doi: 10.5897/AJAR2020.15203
Marstaller, J., Tausch, F. e Stock, S. C. (2019) “DeepBees - Building and Scaling Convolutional Neuronal Nets For Fast and Large-Scale Visual Monitoring of Bee Hives”, in ICCV Workshop, October. doi: 10.1109/ICCVW.2019.00036
Pimentel, A. D. A., Absy, M. L., Rech, A. R. e de Abreu, V. H. R. (2020) “Pollen sources used by Frieseomelitta Ihering 1912 (Hymenoptera: Apidae: Meliponini) bees along the course of the Rio Negro, Amazonas, Brazil”, Acta Bot. Brasilica, vol. 34, no. June, p. 371-383. doi: 10.1590/0102-33062019abb0391
Qing, Y., Jin, F., Jian, T., Wei-gen, X., Xu-hua, Z., Bao-jun, Y., Jun, L., Yi-ze, X., Bo1, Y., Shu-zhen, W., Nai-yang, K. e Li-jun, W. (2020) “Development of an automatic monitoring system for rice light-trap pests based on machine vision”, J. Integr. Agric., vol. 19, no. February, p. 2500-2513. doi: 10.1016/S2095-3119(20)63168-9
Redmon, J. e Farhadi, A. (2016) “YOLO9000: Better, Faster, Stronger”, Cornell Univ., no. December. arXiv:1612.08242v1
Redmon, J. e Farhadi, A. (2018) “YOLOv3: An Incremental Improvement”, Cornell Univ., no. April. arXiv:1804.02767v1
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2015) “You Only Look Once: Unified, Real-Time Object Detection”, Cornell Univ., no. May. arXiv:1506.02640v5
Sánchez-bayo, F. e Wyckhuys, K. A. G. (2019) “Worldwide decline of the entomofauna: A review of its drivers”, Biol. Conserv., vol. 232, no. September, p. 8-27. doi: https://doi.org/10.1016/j.biocon.2019.01.020
Shen, Y., Zhou, H., Li, J., Jian, F. e Jayas, D. S. (2018) “Detection of stored-grain insects using deep learning”, Comput. Electron. Agric., vol. 145, no. October, p. 319-325. doi: https://doi.org/10.1016/j.compag.2017.11.039
Xia, D., Chen, P., Wang, B., Zhang, J. e Xie, C. (2018) “Insect Detection and Classification Based on an Improved Convolutional Neural Network”, Sensors, vol. 18, no. November, p. 1-12. doi: 10.3390/s18124169
Publicado
18/07/2021
Como Citar
LEOCÁDIO, Rodolfo R. V.; SEGUNDO, Alan K. R.; SOUZA, Jefferson R.; GALASCHI-TEIXEIRA, Juliana; SOUZA, Paulo de; PESSIN, Gustavo.
Detecção de Abelhas Nativas em Colmeias em Campo Utilizando Visão Computacional. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 12. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 59-68.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2021.15737.
