Supervised Learning Algorithms Evaluation on Sweet Potato Data for Production Indices Considering Human Consumption

Mardem Arantes de Castro; Ranulfo Mascari Neto; Joaquim Quinteiro Uchôa; Jesimar da Silva Arantes; Orlando Gonçalves Brito; Valter Carvalho de Andrade Júnior; Jeferson Carlos de Oliveira Silva; Renato Ramos da Silva

doi:10.22456/2175-2745.140067

Authors

Mardem Arantes de Castro Federal University of Lavras (UFLA)
Ranulfo Mascari Neto Federal University of Lavras (UFLA)
Joaquim Quinteiro Uchôa Federal University of Lavras (UFLA)
Jesimar da Silva Arantes Federal University of Lavras (UFLA)
Orlando Gonçalves Brito Federal University of Lavras (UFLA)
Valter Carvalho de Andrade Júnior Federal University of Lavras (UFLA)
Jeferson Carlos de Oliveira Silva Federal University of Lavras (UFLA)
Renato Ramos da Silva Federal University of Lavras (UFLA)

DOI:

https://doi.org/10.22456/2175-2745.140067

Keywords:

Sweet Potato, Supervised learning algorithms, Machine Learning

Abstract

The sweet potato holds significant importance as a root crop cultivated globally, serving purposes in both human and animal nutrition, ethanol fuel production, and ornamental cultivation. Several genetic experiments have been undertaken to identify superior root varieties. Within this study, a refined statistical methodology has been devised to mitigate environmental factors and promote equitable comparisons among diverse varieties. Through the integration of supplementary environmental data into the experimental framework, a deeper understanding of each variety can be attained. This research signifies an inaugural endeavor towards scrutinizing post-harvest sweet potato data, aiming to unveil novel insights.

Downloads

Download data is not yet available.

References

FOOD, T.; (FAO), A. O. FAO Statistics online database (FAOSTAT). 2023. Accessed: 09/08/2023. Disponível em: https://www.fao.org/faostat/en/#data/QCL.

FERNANDES, A. et al. Sistema de produção de batata-doce. Brasília, DF: Embrapa Hortaliças, 2021., 2021.

WHAT is Food Security? There are Four Dimensions. 2023. Disponível em: https://www.worldbank.org/en/topic/agriculture/brief/food-security-update/what-is-food-security#:∼:text=Based%20on%20the%201996%20World,an%20active%20and%20healthy%20life. Accessed: 09/11/2023.

VIEIRA, T. d. S. et al. Efeito da substituição da farinha de trigo no desenvolvimento de biscoitos sem gluten. Brazilian Journal of Food Technology, SciELO Brasil, v. 18, p. 285–292, 2015.

ALPAYDIN, E. Introduction to machine learning. [S.l.]: MIT press, 2020.

MELLO, A. F. S. A importância socioeconômica da batata-doce para a agricultura brasileira. Brasília: Embrapa Hortaliças, 2015. Accessed: 13/01/2024.

VIZZOTTO, M. et al. Physicochemical and antioxidant capacity analysis of colored sweet potato genotypes: In natura and thermally processed. Ciência Rural, v. 47, 01 2017.

LAURIE, S. M.; FABER, M.; CLAASEN, N. Incorporating orange-fleshed sweet potato into the food system as a strategy for improved nutrition: The context of South Africa. Food Research International, v. 104, p. 77–85, 2018. ISSN 0963-9969. Balanced diets in food systems: emerging trends and challenges for human health and well-being. Disponível em: https://www.sciencedirect.com/science/article/pii/S0963996917305781.

ZHANG, X. et al. The modulatory effect of anthocyanins from purple sweet potato on human intestinal microbiota in vitro. Journal of Agricultural and Food Chemistry, v. 64, 03 2016.

GRÜNEBERG, P.; MWANGA, R.; ESPINOZA, J. Breeding clonally propagated crops. In: Plant breeding and farmer participation. Rome, Italy: Food and Agriculture Organization of the United Nations, 2009. cap. 13, p. 275–322.

FERREIRA, A. A. A. D. L. G. de C. E. Classificação e tendência climática em Lavras, MG. Ciência e Agrotecnologia, v. 31, n. 6, p. 1862–1866, 2007.

SILVA, J. C. D. O. Seleção de clones de batata-doce para diferentes aptidões agronômicas. Tese (Master thesis) — Universidade Federal de Lavras, Lavras, MG, October 2019. Available at http://repositorio.ufla.br/handle/1/37472.

GERON, A. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. [S.l.]: O’Reilly Media, Inc., 2022.

VEMURI, V. K. The hundred-page machine learning book. Journal of Information Technology Case and Application Research, Taylor & Francis, v. 22, n. 2, p. 136–138, 2020.

LIU, Y.; WANG, Y.; ZHANG, J. New machine learning algorithm: Random forest. In: LIU, B.; MA, M.; CHANG, J. (Ed.). Information Computing and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. p. 246–252. ISBN 978-3-642-34062-8.

BREIMAN, L. Random forests. Machine Learning, v. 45, n. 1, p. 5–32, Oct 2001. ISSN 1573-0565. Disponível em: https://doi.org/10.1023/A:1010933404324.

SOMVANSHI, M. et al. A review of machine learning techniques using decision tree and support vector machine. In: IEEE. 2016 international conference on computing communication control and automation (ICCUBEA). [S.l.], 2016. p. 1–7.

GAMMERMANN, A. Support vector machine learning algorithm and transduction. Computational Statistics, v. 15, n. 1, p. 31–39, Mar 2000. ISSN 1613-9658. Disponível em: https://doi.org/10.1007/s001800050034.

GERALDO-CAMPOS, L. A.; SORIA, J. J.; PANDO-EZCURRA, T. Machine learning for credit risk in the reactive Peru program: a comparison of the lasso and ridge regression models. Economies, MDPI, v. 10, n. 8, p. 188, 2022.

MUTHUKRISHNAN, R.; ROHINI, R. Lasso: A feature selection technique in predictive modeling for machine learning. In: IEEE. 2016 IEEE international conference on advances in computer applications (ICACA). [S.l.], 2016. p. 18–20.

ZHOU, Z.-H. Machine learning. [S.l.]: Springer Nature, 2021.

GLEN, S. RMSE: Root Mean Square Error. 2023. From StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/probability-and-statistics/regression-analysis/rmse-root-mean-square-error/. Accessed in: November 28th.

COSTA, A. L. d. et al. Selection of superior sweet potato genotypes for human consumption via mixed models. Bragantia, SciELO Brasil, v. 81, p. e4122, 2022.

SILVA, J. C. de O. et al. Mixed model-based indices for selection of sweet potato genotypes for different agronomic aptitudes. Euphytica, Springer, v. 218, n. 7, p. 86, 2022.

DONATO, L. M. S. et al. Uso de ramas de batata-doce para produção de feno. Ciência Animal Brasileira, SciELO Brasil, v. 21, p. e–53493, 2020.

VIEIRA, A. D. et al. Avaliação agronômica de clones de batata doce com potencial para produção de etanol. Brazilian Journal of Applied Technology for Agricultural Science/Revista Brasileira de Tecnologia Aplicada nas Ciências Agrárias, v. 8, n. 1, 2015.

JUNIOR, V. C. de A. et al. Características produtivas e qualitativas de ramas e raízes de batata-doce. Horticultura Brasileira, SciELO Brasil, v. 30, p. 584–589, 2012.

AONO, A. H. et al. A divide-and-conquer approach for genomic prediction in rubber tree using machine learning. Scientific Reports, v. 12, n. 1, p. 18023, Oct 2022. ISSN 2045-2322. Disponível em: https://doi.org/10.1038/s41598-022-20416-z.

AONO, A. H. et al. A joint learning approach for genomic prediction in polyploid grasses. Scientific Reports, v. 12, n. 1, p. 12499, Jul 2022. ISSN 2045-2322. Disponível em: https://doi.org/10.1038/s41598-022-16417-7.