Effects of agro-sensor time series approximation on plant stress detection: an experimental study
Resumo
This paper describes an experimental study on the effect of reducing time series collected from IoT electrical agro-sensors through approximation techniques, in time series classification tasks, for plant stress detection. From large sets of real data, stored in time series format, experiments were carried out to analyze: (i) performance of mathematical methods to reduce the dimensionality of time series - PAA, SAX and MCB; and (ii) Whether the application of these techniques influences the performance of time series classification models for plant stress detection, using machine learning algorithms KNN, SVM and ANN. Both in terms of data volume reduction and time series classification, the experiment showed significant improvements in terms of compression rate and accuracy, with the best result found in the use of PAA+SAX techniques for reduction and SVM for classification.
Palavras-chave:
time series, time series aproximation, machine learning, IoT sensing, plant stress detection
Referências
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Schäfer, P. and Högqvist, M. (2012). Sfa: A symbolic fourier approximation and index for similarity search in high dimensional datasets. pages 516 – 527. 10.1145/2247596.2247656
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Al-Qurabat, A. K. M. and Kadhum Idrees, A. (2020). Data gathering and aggregation with selective transmission technique to optimize the lifetime of internet of things networks. International Journal of Communication Systems, 33(11):e4408. DOI: http://doi.org/10.1002/dac.4408
Blalock, D., Madden, S., and Guttag, J. (2018). Sprintz: Time series compression for the internet of things. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3):1–23. DOI: http://doi.org/10.1145/3264903
Faouzi, J. and Janati, H. (2020). pyts: A python package for time series classification. Journal of Machine Learning Research, 21(46):1–6.
Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. (2002). Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 3. DOI: http://doi.org/10.1007/PL00011669
Krawczak, M. and Szkatuła, G. (2014). An approach to dimensionality reduction in time series. Information Sciences, 260:15–36. DOI: http://doi.org/10.1016/j.ins.2013.10.037
Lelewer, D. A. and Hirschberg, D. S. (1987). Data compression. ACM Comput. Surv., 19(3):261–296. DOI: https://doi.org/10.1145/45072.45074
Lin, J., Keogh, E., Wei, L., and Lonardi, S. (2007). Experiencing sax: A novel symbolic representation of time series. Data Min. Knowl. Discov., 15:107–144. DOI: https://doi.org/10.1007/s10618-007-0064-z
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830. DOI: https://doi.org/10.5555/1953048.2078195
Schäfer, P. and Högqvist, M. (2012). Sfa: A symbolic fourier approximation and index for similarity search in high dimensional datasets. pages 516 – 527. 10.1145/2247596.2247656
Tobore, I., Kandwal, A., Li, J., Yan, Y., Omisore, O. M., Enitan, E., Sinan, L., Yuhang, L., Wang, L., and Nie, Z. (2020). Towards adequate prediction of prediabetes using spatiotemporal ecg and eeg feature analysis and weight-based multi-model approach. Knowledge-Based Systems, 209:106464. DOI: https://doi.org/10.1016/j.knosys.2020.106464
Toledo, G. R. A. (2019). Caracterização eletrofisiológica do feijão (Phaseolus vulgaris L.) cv. BRS-Expedito sob diferentes disponibilidades hídricas. PhD thesis, UFPel, Pelotas.
Xin, B., Peng, W., Kwon, Y., and Liu, Y. (2019). Modeling, discretization, and hyperchaos detection of conformable derivative approach to a financial system with market confidence and ethics risk. Advances in Difference Equations, 2019(1):1–14. DOI: https://doi.org/10.1186/s13662-019-2074-8
Zhang, X., Cao, Z., and Dong, W. (2020). Overview of edge computing in the agricultural internet of things: Key technologies, applications, challenges. IEEE Access, 8:141748–141761. DOI: https://doi.org/10.1109/ACCESS.2020.3013005
Publicado
10/11/2021
Como Citar
OLIVEIRA JÚNIOR, Marcos A.; SEDREZ, Gregory; MONTEIRO, Anderson; PUNTEL, Fernando Emilio; CAVALHEIRO, Gerson Geraldo H..
Effects of agro-sensor time series approximation on plant stress detection: an experimental study. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 99-107.
ISSN 2177-9724.
DOI: https://doi.org/10.5753/sbiagro.2021.18380.
