References
[1] Sevegnani KB. Zootecnia de precisão: desafios para a produção animal. In: Tecnologia e inovação na agricultura: aplicação, produtividade, e sustentabilidade em pesquisa. Editora Científica; 2023. p. 258-71.
[2] Berckmans D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev Sci Tech. 2014;33(1):189-207.
[3] Goap A, Sharma D, Shukla AK, Krishna CR. An IoT based smart irrigation management system using Machine learning and open source technologies. Comput Electron Agric. 2018;155:41-9.
[4] Krishnamoorthy R, Thiagarajan R, Padmapriya S, Mohan I, Arun S, Dineshkumar T. Applications of machine learning and deep learning in smart agriculture. In: Machine learning algorithms for signal and image processing. IEEE; 2022. p. 371-95.
[5] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
[6] Ünal Z. Smart farming becomes even smarter with deep learning—A bibliographical analysis. IEEE Access. 2020;8:105587-609.
[7] Lin J, Zhu L, Chen WM, Wang WC, Han S. Tiny machine learning: progress and futures. IEEE Circuits Syst Mag. 2023;23(3):8-34.
[8] Banbury CR, Reddi VJ, Lam M, Fu W, Fazel A, Holleman J, et al. Benchmarking TinyML systems: challenges and direction. arXiv [Preprint]. 2021; arXiv:2003.04821. Available from: https://arxiv.org/abs/2003.04821
[9] Zennaro M, Nduati R, Masera M, Nkenyereye L. Machine learning on low-power embedded devices for the African context: a review. IEEE Access. 2022;10:21450-62.
[10] David R, Duke J, Jain A, Reddi VJ, Jeffries N, Li J, et al. TensorFlow Lite Micro: embedded machine learning for TinyML systems. Proc Mach Learn Syst. 2021;3:800-11.
[11] Ramírez-Hernández A, Uchoa F, de Azevedo Serpa MC, Binder LC, Rodrigues AC, Szabó MPJ, et al. Clinical and serological evaluation of capybaras (Hydrochoerus hydrochaeris) successively exposed to an Amblyomma sculptum-derived strain of Rickettsia rickettsii. Sci Rep. 2020;10(1):924.
[12] Lessa DAM, Moreira-Soto A, Ramos DGS, Krawczak FS, Pacheco TA, Martins MM, et al. Brazilian Spotted Fever: A re-emerging public health threat. Front Public Health. 2021;9:638000.
[13] Labruna MB. Ecology of Rickettsiain South America. Ann N Y Acad Sci. 2009;1166:156-66.
[14] SINAN/SVS/MS. Sistema de Informação de Agravos de Notificação: Febre Maculosa Brasileira. Ministério da Saúde do Brasil; 2023 [cited 2026 Apr 14]. Available from: https://portalsinan.saude.gov.br
[15] Torralba A, Efros AA. Unbiased look at dataset bias. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2011:1521-8.
[16] Hymel S, Banbury CR, Situnayake D, Elium A, Ward C, Kelcey M, et al. Edge Impulse: an MLOps platform for tiny machine learning. arXiv [Preprint]. 2022; arXiv:2212.03332. Available from: https://arxiv.org/abs/2212.03332
[17] Raschka S. Model evaluation, model selection, and algorithm selection in machine learning. arXiv [Preprint]. 2020; arXiv:1811.12808. Available from: https://arxiv.org/abs/1811.12808
[18] Shahinfar S, Meek P, Falzon G. “How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecol Inform. 2020;57:101085.
[19] Yadav S, Bhole GP. Handling imbalanced dataset classification in machine learning. 2020 IEEE Pune Sect Int Conf (PuneCon). 2020:1-6.
[20] Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv [Preprint]. 2015; arXiv:1412.6980. Available from: https://arxiv.org/abs/1412.6980
[21] Chowdhery A, Warden P, Shlens J, Howard A, Rhodes R. Visual Wake Words Dataset. arXiv [Preprint]. 2019; arXiv:1906.05721. Available from: https://arxiv.org/abs/1906.05721
[22] Espressif Systems. ESP32-CAM: Technical Reference Manual. Espressif Systems Co., Ltd.; 2023.