Computational intelligence to study the importance of characteristics in flood-irrigated rice
Resumo
The study of traits in crops enables breeders to guide strategies for selecting and accelerating the progress of genetic breeding. Although the simultaneous evaluation of characteristics in the plant breeding programme provides large quantities of information, identifying which phenotypic characteristic is the most important is a challenge facing breeders. Thus, this work aims to quantify the best approaches for prediction and establish a network of better predictive power in flood-irrigated rice via methodologies based on regression, artificial intelligence, and machine learning. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the characteristics. Computational intelligence and machine learning were notable for their ability to extract nonlinear information from model inputs. Predicting the relative contribution of auxiliary characteristics in rice through computational intelligence and machine learning proved to be efficient in determining the relative importance of variables in flood-irrigated rice. The characteristics indicated to assist in decision making are flowering, number of grains filled by panicles and length of panicles for this study. The network with only one hidden layer with 15 neurons was observed to be efficient in determining the relative importance of variables in flooded rice.
Downloads
Referências
Anacleto, R. Cuevas, R. P., Jimenez, R., Llorente, C., Nissila, E., Henry, R., Sreenivasulu, N. (2015). Prospects of breeding high-quality rice using post-genomic tools. Theoretical and Applied Genetics, 128(8), 1449-1466. DOI: https://doi.org/10.1007/s00122-015-2537-6
Beck, M. W. (2018). NeuralNetTools: Visualization and analysis tools for neural networks. Journal of Statistical, 85(11), 1-20. DOI: http://dx.doi.org/10.18637 / jss.v085.i11
Beucher, A., Møller, A. B., & Greve, M. H. (2019). Artificial neural networks and decision tree classification for predicting soil drainage classes in Denmark. Geoderma, 352, 351-359. DOI: https://doi.org/10.1016/j.geoderma.2017.11.004
Cruz, C. D. (2016). Genes Software – extended and integrated with the R, Matlab and Selegen. Acta Scientiarum. Agronomy, 38(4), 547-552. DOI: http://dx.doi.org/10.4025/actasciagron.v38i4.32629
Cruz, C. D., & Nascimento, M. (2018). Inteligência computacional aplicada ao melhoramento genético. Viçosa, MG: Editora UFV.
De Oña, J., & Garrido, C. (2014). Extracting the contribution of independent variables in neural network models: a new approach to handle instability. Neural Computing and Applications, 25(3-4), 859-869. DOI: https://doi.org/10.1007/s00521-014-1573-5
Degenhardt, F., Seifert, S., & Szymczak, S. (2019). Evaluation of variable selection methods for random forests and omics data sets. Briefings in Bioinformatics, 20(2), 492-503. DOI: https://doi.org/10.1093/bib/bbx124
Evans, L. E., & Bhatt, G. M. (1977). Influence of seed size, protein content and cultivar on early seedling vigor in rice. Canadian Journal of Plant Science, 57(3), 929-935. DOI: https://doi.org/10.4141/cjps77-133
Fan, C., Xing, Y., Mao, H., Lu, T., Han, B., Xu, C., … Zhang, Q. (2006). GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theoretical and Applied Genetics, 112(6), 1164-1171. DOI: https://doi.org/10.1007/s00122-006-0218-1
Ferreira, M. G., Azevedo, A. M., Siman, L. I., Silva, G. H., Carneiro, C. S., Alves, F. M., … Nick, C. (2017). Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks. Scientia Agricola, 73(3), 203-207. DOI: http://dx.doi.org/10.1590/1678-992X-2015-0451
Freitas, J. G., Cantarella, H., Salomon, M. V., Malovolta, V. M. A., Castro, L. H. S. M., Gallo, P. B., & Azzini, L. E. (2007). Produtividade de cultivares de arroz irrigado resultante da aplicação de doses de nitrogênio. Bragantia, 66(2), 317-325. DOI: http://dx.doi.org/10.1590/S0006-87052007000200016
Garson, G. D. (1991). Interpreting neural network connection weights. Artificial Intelligence Expert, 6, 46-51.
Gedeon, T. D., Wong, P. M., & Harris, D. (1995). Balancing bias and variance: network topology and pattern set reduction techniques. Berlin, Heidelberg, GE: Springer Berlin Heidelberg.
Ghani, I. M. M., & Ahmad, S. (2010). Stepwise multiple regression method to forecast fish landing. Procedia - Social and Behavioral Sciences, 8, 549-554. DOI: https://doi.org/10.1016/j.sbspro.2010.12.076
Gianola, D., Okut, H., Weigel, K. A., & Rosa, G. J. M. (2011). Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genetics, 12(87), 1-14. DOI: https://doi.org/10.1186/1471-2156-12-87
Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3),143-151. DOI: https://doi.org/10.1016/0954-1810(94)00011-S
Gregorutti, B., Michel, B., & Saint-Pierre, P. (2017). Correlation and variable importance in random forests. Statistics and Computing, 27, 659-678. DOI: https://doi.org/10.1007/s11222-016-9646-1
Haddouche, R., Chetate, B., & Said Boumedine, M. (2018). Neural network ARX model for gas conditioning tower. International Journal of Modeling and Simulation, 39(3), 166-177. DOI: https://doi.org/10.1080/02286203.2018.1538848
Hassanzadeh, Z., Ghavami, R., & Kompany-Zareh, M. (2015). Radial basis function neural networks based on the projection pursuit and principal component analysis approaches: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors. Medicinal Chemistry Research, 25, 19-29. DOI: https://doi.org/10.1007/s00044-015-1466-x
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statiscal learning data mining, inference, and prediction (2nd ed.). New York, NY: Springer.
Huang, X., Zhao, Y. Wei, X., Li, C., Wang, A., Zhao, Q., … Han, B. (2012a) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nature Genetics, 44, 32-39. DOI: https://doi.org/10.1038/ng.1018
Li, L., & Zha, Y. (2019). Estimating monthly average temperature by remote sensing in China. Advances in Space Research, 63(8), 2345-2357. DOI: https://doi.org/10.1016/j.asr.2018.12.039
Matlab. (2016). Software. Natick, MA: The MathWorks Inc.
Misra, G., Badoni, S., Anacleto, R., Graner, A., Alexandrov, N., & Sreenivasulu, N. (2017). Whole genome sequencing-based association study to unravel genetic architecture of cooked grain width and length traits in rice. Scientific Reports, 7(12478), 1-16. DOI: https://doi.org/10.1038/s41598-017-12778-6
Ntanos, D. A., & Koutroubas, S. D. (2002). Dry matter and Naccumulation and translocation for Indica and Japonica riceunder Mediterranean conditions. Field Crops Research, 74(1), 93-101. DOI: https://doi.org/10.1016/S0378-4290(01)00203-9
Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: a randomization approach for understanding variable contributions in artifical neural networks. Ecological Modelling, 154(1–2), 135-150. DOI: https://doi.org/10.1016/s0304-3800(02)00064-9
Osco, L. P., Ramos, A. P. M., Moriya, E. A. S., Bavaresco, L. G., Lima, B. C., Estrabis, N., ... Araújo, F. F. (2019). Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks. Remote Sensing, 11(23), 1-15. DOI: https://doi.org/10.3390/rs11232797
Osco, L. P., Ramos, A. P. M., Pinheiro, M. M. F., Moriya, E. A. S., Imai, N. N., Estrabis, N., … Creste, J. E. (2020). A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurement. Remote Sensing, 12(6), 1-21. DOI: http://dx.doi.org/10.3390/rs12060906
Paliwal, M. & Kumar, U. A. (2011). Assessing the contribution of variables in feed forward neural network. Applied Soft Computing, 11, 3690-3696.
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), 1-12. DOI: https://doi.org/10.1038/s41598-019-53451-4
Paruelo, J. M., & Tomasel, F. (1997). Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecological Modelling, 98(2-3), 173-186. DOI: https://doi.org/10.1016/s0304-3800(96)01913-8
Porwal, A., Carranza, E. J. M., & Hale, M. (2003). Artificial neural networks for mineral potential mapping; a case study from Aravalli Province, Western India. Natural Resources Research, 12(3), 155-171. DOI: https://doi.org/10.1023/A:1025171803637
Quinlan, J. R. (1996). Learning decision tree classifiers. ACM Computing Surveys, 28(1), 71-72. DOI: https://doi.org/10.1145/234313.234346
Roy, P. P., & Roy, K. (2008). On some aspects of variable selection for partial least squares regression models. QSAR & Combinatorial Science, 27(3), 302-313. DOI: https://doi.org/10.1002/qsar.200710043
Sant’Anna, I. C., Ferreira, R. A. D. C., Nascimento, M., Carneiro, V. Q., Silva, G. N., Cruz, C. D., ... Chagas, F. E. O. (2019). Multigenerational prediction of genetic values using genome-enabled prediction. PLoS ONE, 14(1), 1-14. DOI: https://doi.org/10.1371/journal.pone.0210531
Santos, R. P, Dean, D. L., Weaver, J. M., & Hovanski, Y. (2018). Identifying the relative importance of predictive variables in artificial neural networks based on data produced through a discrete event simulation of a manufacturing environment. International Journal of Modelling and Simulation, 39(4), 234-245. DOI: https://doi.org/10.1080/02286203.2018.1558736
Silva, G. N., Nascimento, M., Sant’Anna, I. C., Cruz, C. D., Caixeta, E. T., Carneiro, P. C. S., ... Oliveira, M. S. (2017). Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee. Pesquisa Agropecuária Brasileira, 52(3), 186-193. DOI: http://dx.doi.org/10.1590/s0100-204x2017000300009
Silva, G. N., Tomaz, R. S., Sant’anna, I. C., Nascimento, M., Bhering, L. L., & Cruz, C. D. (2014). Neural networks for predicting breeding values and genetic gains. Scientia Agricola, 71(6), 494-498. DOI: http://dx.doi.org/10.1590/0103-9016-2014-0057
Skawsang, S., Nagai, M., Nitin, K., & Soni, P. (2019). Predicting rice pest population occurrence with satellite-derived crop phenology, ground meteorological observation, and machine learning: A case study for the central plain of Thailand. Applied Sciences, 9(22), 1-19. DOI: https://doi.org/10.3390/app9224846
Somers, M. J., & Casal, J.C. (2009). Using artificial neural networks to model nonlinearity: The case of the job satisfaction-job performance relationship. Organizational Research Methods, 12(3), 403-417. DOI: https://doi.org/10.1177/1094428107309326
Sousa, I. C., Nascimento, M., Silva, G. N., Nascimento, A. C. C., Cruz, C. D., Fonseca, F., ... Caixeta, E. T. (2020). Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. Scientia Agricola, 78(4), 1-8. DOI: http://dx.doi.org/10.1590/1678-992x-2020-0021
Tan, K., Li, E., Du, Q., & Du, P. (2014). An efficient semi-supervised classification approach for hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 97, 36-45. http://dx.doi.org/10.1016/j.isprsjprs.2014.08.003.
Tsang, M., Cheng, D., & Liu, Y. (2017). Detecting statistical interactions from neural network weights. In 6th International Conference on Learning Representations (p. 1-21). Vancouver, CA: ICLR. DOI: https://doi.org/10.48550/arXiv.1705.04977
Yu, H., Campbell, M. T., Zhang, Q., Walia, H., & Morota, G. (2019). Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. G3: Genes, Genomes, Genetics, 9(6), 1975-1986. DOI: https://doi.org/10.1534/g3.119.400154
Copyright (c) 2023 Acta Scientiarum. Agronomy
This work is licensed under a Creative Commons Attribution 4.0 International License.
DECLARAÇÃO DE ORIGINALIDADE E DIREITOS AUTORAIS
Declaro que o presente artigo é original, não tendo sido submetido à publicação em qualquer outro periódico nacional ou internacional, quer seja em parte ou em sua totalidade.
Os direitos autorais pertencem exclusivamente aos autores. Os direitos de licenciamento utilizados pelo periódico é a licença Creative Commons Attribution 4.0 (CC BY 4.0): são permitidos o compartilhamento (cópia e distribuição do material em qualqer meio ou formato) e adaptação (remix, transformação e criação de material a partir do conteúdo assim licenciado para quaisquer fins, inclusive comerciais.
Recomenda-se a leitura desse link para maiores informações sobre o tema: fornecimento de créditos e referências de forma correta, entre outros detalhes cruciais para uso adequado do material licenciado.