A qualitative Decision Tree model for Common Beans and Cowpea classification
DOI:
https://doi.org/10.46420/TAES.e240004Keywords:
Phaseolus vulgaris L., Vigna unguiculata L. Walp., selection, machine learningAbstract
Common beans and cowpea are two grains that form part of the preferred diet in several countries, mainly due to their nutritional value. Knowledge of their diversity is important for plant breeding and determines the conservation and use strategy. Previous analyzes show that there is variability for a set of qualitative and quantitative descriptors for this species. The objective of these work was to use data from qualitative descriptors to generate a decision tree model that makes it possible to classify common bean and cowpea genotypes. 17 bean genotypes were used, 12 of which were common beans and 5 were cowpeas. Eight qualitative descriptors were used to characterize the bean genotypes. Machine learning techniques were used to generate decision tree models for classifying bean genotypes. Using the accuracy, precision and F1-score metrics in the cross-validation approach, we select the best decision tree model. This model was adapted into a flowchart for use in various purposes, aiming to classify beans based on qualitative descriptors.
References
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
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Trends in Agricultural and Environmental Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
This is a human-readable summary of (and not a substitute for) the license.
