Um modelo qualitativo de árvore de decisão para classificação de feijão comum e feijão-caupi

Autores

DOI:

https://doi.org/10.46420/TAES.e240004

Palavras-chave:

Phaseolus vulgaris L., Vigna unguiculata L. Walp., selection, machine learning

Resumo

O feijão comum e o feijão-caupi são dois grãos que fazem parte da alimentação preferida em vários países, principalmente pelo seu valor nutricional. O conhecimento de sua diversidade é importante para o melhoramento de plantas e determina a estratégia de conservação e uso. Análises anteriores mostram que existe variabilidade para um conjunto de descritores qualitativos e quantitativos para esta espécie. O objetivo deste trabalho foi utilizar dados de descritores qualitativos para gerar um modelo de árvore de decisão que possibilite a classificação de genótipos de feijão comum e feijão-caupi. Foram utilizados 17 genótipos de feijão, sendo 12 de feijão comum e 5 de feijão-caupi. Foram utilizados oito descritores qualitativos para caracterizar os genótipos de feijão. Técnicas de aprendizado de máquina foram utilizadas para gerar modelos de árvores de decisão para classificação de genótipos de feijão. Usando as métricas de exatidão, precisão e pontuação F1 na abordagem de validação cruzada, selecionamos o melhor modelo de árvore de decisão. Este modelo foi adaptado em um fluxograma para utilização em diversos fins, visando classificar os grãos com base em descritores qualitativos.

Biografia do Autor

  • Jorge González Aguilera, UEMS

    Universidade Estadual de Mato Grosso do Sul/UEMS, Departamento de Agronomia, Cassilândia, MS, Brasil

  • Fabio Steiner, UEMS

    Universidade Estadual de Mato Grosso do Sul/UEMS, Departamento de Agronomia, Cassilândia, MS, Brasil

  • Diógenes Martins Bardiviesso, UEMS

    Universidade Estadual de Mato Grosso do Sul/UEMS, Departamento de Agronomia, Cassilândia, MS, Brasil

  • Luis Morales-Aranibar, UNIQ

    Universidad Nacional Intercultural de Quillabamba (UNIQ), Cusco, Perú

  • Leandris Argentel-Martínez, TECNM

    Tecnológico Nacional de México, Instituto Tecnológico del Valle del Yaqui, Bácum, Sonora, México

Referências

Abebe, B. K., & Alemayehu, M. T. (2022). A review of the nutritional use of cowpea (Vigna unguiculata L. Walp) for human and animal diets. Journal of Agriculture and Food Research, 10, 100383. https://doi.org/10.1016/J.JAFR.2022.100383

Aguilera, J. G., Barbosa, E., Ribeiro, E., Vidal Do Nascimento, A. C., Silva, M. V., Dos, R., Carvalho, S., Santos Cocco, A., Fernanda, A., Barreto, S., Martins, G. S., Pereira Barcelos, R., Augusto, J., Rodrigues, S., Steiner, F., & Martins Bardiviesso, D. (2023). Qualitative and quantitative descriptors for quantifying the genetic diversity of bean seeds. https://doi.org/10.46420/TAES.e230001

Aguilera, J. G., Marim, B. G., Setotaw, T. A., Zuffo, A. M., Nick, C., & da Silva, D. J. H. (2019). The combination of data as a strategy to determine the diversity of tomato subsambples. Amazonian Journal of Plant Research, 3(1), 276–289. https://doi.org/10.26545/ajpr.2019.b00035x

Boukar, O., Abberton, M., Oyatomi, O., Togola, A., Tripathi, L., & Fatokun, C. (2020). Introgression Breeding in Cowpea [Vigna unguiculata (L.) Walp.]. Frontiers in Plant Science, 11, 567425. https://doi.org/10.3389/FPLS.2020.567425/BIBTEX

Cabral, P. D. S., Soares, T. C. B., Lima, A. B. de P., Alves, D. de S., & Nunes, J. A. (2011). Diversidade genética de acessos de feijão comum por caracteres agronômicos. Revista Ciência Agronômica, 42(4), 898–905. https://doi.org/10.1590/S1806-66902011000400011

Catarino, S., Brilhante, M., Essoh, A. P., Charrua, A. B., Rangel, J., Roxo, G., Varela, E., Moldão, M., Ribeiro-Barros, A., Bandeira, S., Moura, M., Talhinhas, P., & Romeiras, M. M. (2021). Exploring physicochemical and cytogenomic diversity of African cowpea and common bean. Scientific Reports 2021 11:1, 11(1), 1–14. https://doi.org/10.1038/s41598-021-91929-2

Coelho, C. M. M., Coimbra, J. L. M., Souza, C. A. de, Bogo, A., & Guidolin, A. F. (2007). Diversidade genética em acessos de feijão (Phaseolus vulgaris L.). Ciência Rural, 37(5), 1241–1247. https://doi.org/10.1590/S0103-84782007000500004

de Oliveira, B. R., da Silva, A. A. P., Teodoro, L. P. R., de Azevedo, G. B., de Oliveira Sousa Azevedo, G. T., Baio, F. H. R., Sobrinho, R. L., da Silva Junior, C. A., & Teodoro, P. E. (2021). Eucalyptus growth recognition using machine learning methods and spectral variables. Forest Ecology and Management, 497, 119496. https://doi.org/10.1016/J.FORECO.2021.119496

de Oliveira, B. R., Zuffo, A. M., Steiner, F., Aguilera, J. G., & Gonzales, H. H. S. (2023). Classification of soybean genotypes during the seedling stage in controlled drought and salt stress environments using the decision tree algorithm. Journal of Agronomy and Crop Science, 209(5), 724–733. https://doi.org/10.1111/jac.12654

de Sousa Leite, W., de Souza Miranda, R., de Moura Rocha, M., Dutra, A. F., Santos, A. S., da Silva, A. C., de Brito, F. M., de Sousa, R. S., de Araújo, A. S. F., do Nascimento, C. W. A., Zuffo, A. M., & de Alcântara Neto, F. (2023). Silicon alleviates drought damage by increasing antioxidant and photosynthetic performance in cowpea. Journal of Agronomy and Crop Science, 209(6), 772–787. https://doi.org/10.1111/jac.12659

Didinger, C., Foster, M. T., Bunning, M., & Thompson, H. J. (2022). Nutrition and Human Health Benefits of Dry Beans and Other Pulses. Dry Beans and Pulses, 481–504. https://doi.org/10.1002/9781119776802.CH19

Elsayed, A. Y. A. M., Hassan, B. A. A., Hassanin, A. A., Zyada, H. G., Ismail, H. E. M., & Aguilera, J. G. (2023). Selection parameters for improvement of yield and quality in tomatillo. Ciência e Agrotecnologia, 47. https://doi.org/10.1590/1413-7054202347013722

Enyiukwu, D. N., Chukwu, L. A., & Bassey, I. N. (2020). Nutrient and anti-nutrient compositions of cowpea (Vigna unguiculata) and mung bean (Vigna radiata) seeds grown in humid Southeast Nigeria: A comparison. International Journal of Tropical Drylands, 4(2). https://doi.org/10.13057/tropdrylands/t040202

Fletcher, S., & Islam, Md. Z. (2020). Decision Tree Classification with Differential Privacy. ACM Computing Surveys, 52(4), 1–33. https://doi.org/10.1145/3337064

Gorunescu, F. (2011). Classification Performance Evaluation (pp. 319–330). https://doi.org/10.1007/978-3-642-19721-5_6

Guimarães, J. B., Nunes, C., Pereira, G., Gomes, A., Nhantumbo, N., Cabrita, P., Matos, J., Simões, F., & Veloso, M. M. (2023). Genetic Diversity and Population Structure of Cowpea (Vigna unguiculata (L.) Walp.) Landraces from Portugal and Mozambique. Plants, 12(4), 846. https://doi.org/10.3390/PLANTS12040846/S1

Hajian-Tilaki, K. (2013). Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian Journal of Internal Medicine, 4(2), 627–635.

Haykin, S. (2009). Neural Networks and Learning Machines (3 rd). Pearson Prentice Hall.

Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: Multi-Label Confusion Matrix. IEEE Access, 10, 19083–19095. https://doi.org/10.1109/ACCESS.2022.3151048

Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. 1137–1143.

Kubat, M. (2021). An Introduction to Machine Learning. An Introduction to Machine Learning, 1–458. https://doi.org/10.1007/978-3-030-81935-4/COVER

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18. https://doi.org/10.3390/s18082674

Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 26 (pp. 431–439). Curran Associates, Inc. http://papers.nips.cc/paper/4928-understanding-variable-importances-in-forests-of-randomized-trees.pdf

Maia, F. R. (2023). Cowpe bean production - (Vigna Unguiculata (l.) walp). A drought-resistant plant is very common in regions of the brazilian semi-arid. Journal of Interdisciplinary Debates, 4(04), 242–263. https://doi.org/10.51249/jid.v4i04.1731

Marin, D. B., Santana, L. S., Barbosa, B. D. S., Barata, R. A. P., Osco, L. P., Ramos, A. P. M., & others. (2021). Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models. Computers and Electronics in Agriculture, 190. https://doi.org/10.1016/j.compag.2021.106476

Ministry of Agriculture. (2009). Ministry of Agriculture, Livestock and Supply. Rules for seed analysis. Ministry of Agriculture, Livestock and Supply. . Secretariat for Agricultural Defense. Brasília: Mapa/A.

Nalini Durga, S., & Usha Rani, K. (2020). A Perspective Overview on Machine Learning Algorithms (pp. 353–364). https://doi.org/10.1007/978-3-030-46939-9_30

Özkan, G., Haliloğlu, K., Türkoğlu, A., Özturk, H. I., Elkoca, E., & Poczai, P. (2022). Determining Genetic Diversity and Population Structure of Common Bean (Phaseolus vulgaris L.) Landraces from Türkiye Using SSR Markers. Genes, 13(8), 1410. https://doi.org/10.3390/GENES13081410/S1

Parmley, K. A., Higgins, R. H., Ganapathysubramanian, B., Sarkar, S., & Singh, A. K. (2019). Machine Learning Approach for Prescriptive Plant Breeding. Scientific Reports, 9(1), 17132. https://doi.org/10.1038/s41598-019-53451-4

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., & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Romero, J. (1961). Varieties of Judias grown in Spain. Ministry of Agriculture.

Sampaio, A. P. L., Aguilera, J. G., Mendes, A. M. da S., Argentel-Martínez, L., Zuffo, A. M., & Teodoro, P. E. (2023). The role of the genetic diversity of Capsicum spp. in the conservation of the species: Qualitative and quantitative characterization. Ciência e Agrotecnologia, 47. https://doi.org/10.1590/1413-7054202347009122

Silva, H. T. (2005). Minimum descriptors to characterize cultivars/varieties of common bean (Phaseolus vulgaris L.). Documentos 184. Embrapa Arroz e Feijão.

Singh, B. B. (2015). Cowpea: The Food Legume of the 21st Century. Cowpea: The Food Legume of the 21st Century, 1–166. https://doi.org/10.2135/2014.cowpea

Sivagama Sundhari, S. (2011). A knowledge discovery using decision tree by Gini coefficient. 2011 International Conference on Business, Engineering and Industrial Applications, 232–235. https://doi.org/10.1109/ICBEIA.2011.5994250

Tangirala, S. (2020). Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm*. International Journal of Advanced Computer Science and Applications, 11(2). https://doi.org/10.14569/IJACSA.2020.0110277

Tariq, A., Yan, J., Gagnon, A. S., Riaz Khan, M., & Mumtaz, F. (2023). Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science, 26(3), 302–320. https://doi.org/10.1080/10095020.2022.2100287

Tavares, T. C. de O., Sousa, S. A. de, Lopes, M. B. S., Veloso, D. A., & Fidelis, R. R. (2018). Divergência genética entre cultivares de feijão comum cultivados no estado do tocantins. REVISTA DE AGRICULTURA NEOTROPICAL, 5(3), 76–82. https://doi.org/10.32404/rean.v5i3.1892

Vilela Barros, P. P., González Aguilera, J., Rodrigues Molina Rezende, J., Cordeiro Taveira, A., Costa Martins, W., Silva Abreu, M., Mario Zuffo, A., & Argentel Martínez, L. (2020). Diversidade Genética Entre Acessos de Mandioca Por Meio de Caracteres Agronômicos. Ensaios e Ciência C Biológicas Agrárias e Da Saúde, 24(1), 29–35. https://doi.org/10.17921/1415-6938.2020v24n1p29-35

Watare, G. W. (2023). Marker assisted Selection for resistance to bean common Mosaic Necrosis virus In French Bean cultivars in Kenya [Http://repository.embuni.ac.ke/handle/embuni/4256]. University of Embu.

Wu, X., Wang, B., Wu, S., Li, S., Zhang, Y., Wang, Y., Li, Y., Wang, J., Wu, X., Lu, Z., & Li, G. (2021). Development of a core set of single nucleotide polymorphism markers for genetic diversity analysis and cultivar fingerprinting in cowpea. Legume Science, 3(3), e93. https://doi.org/10.1002/LEG3.93

Yeganeh-Bakhtiary, A., EyvazOghli, H., Shabakhty, N., Kamranzad, B., & Abolfathi, S. (2022). Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios. Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems 2022, 2022(Special Issue).

Yoosefzadeh Najafabadi, M., Hesami, M., & Eskandari, M. (2023). Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs. Genes, 14(4), 777. https://doi.org/10.3390/genes14040777

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Publicado

2024-04-26

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Volume 2 (2024)

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Seção Artigos

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