Machine learning aplicado à predição da arquitetura radicular de cultivares de soja sob duas condições de disponibilidade hídrica
DOI:
https://doi.org/10.5433/1679-0359.2022v43n3p1017Palavras-chave:
Deficit hídrico, Glycine max L., Morfologia de raiz, Multitask learning.Resumo
O objetivo deste estudo foi avaliar o desempenho de quatro modelos de machine learning, bem como multitask learning, para predizer variáveis radiculares de soja a partir de variáveis simples, em duas condições de disponibilidade hídrica. Para isso,100 cultivares de soja foram conduzidas em casa de vegetação sob uma condição controle e uma condição estresse. Foram avaliadas as variáveis da parte aérea e da raiz. Os modelos machine learning usados para predizer variáveis complexas do sistema radicular foram rede neural artificial (RNA), random forest (RF), extreme gradient boosting (EGBoost) e support vector machine (SVM). O modelo linear foi usado para fins de comparação. O multitask learning foi empregado para RNA e RF. Além disso, a importância das variáveis foi definida usando algoritmos RF e XGBoost. Todos os modelos de machine learning apresentaram melhor desempenho do que o modelo linear. Em geral, SVM apresentou o maior potencial de predição da maioria das variáveis raiz, com melhores valores de RMSE, MAE e R2. O peso seco da parte aérea e o volume da raiz exibiram as maiores importâncias nas predições. Os modelos desenvolvidos por meio do multitask learning apresentaram desempenhos semelhantes aos desenvolvidos convencionalmente. Por fim, conclui-se que os modelos de machine learning avaliados podem ser usados para predizer variáveis radiculares de soja a partir de variáveis facilmente mensuráveis, como massa seca da parte aérea e volume radicular.Downloads
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Referências
Belgiu, M., & Dragu, L. (2016). Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. doi: 10.1016/j.isprsjprs. 2016.01.011
Bressan, T. S., Souza, K. M., Girelli, T. J., Chemale, F. Jr. (2020). Evaluation of machine learning methods for lithology classification using geophysical data. Computers and Geosciences, 139, 104475. doi: 10.10 16/j.cageo.2020.104475
Carmona, P., Climent, F., & Momparler, A. (2019). Predicting failure in the U.S. banking sector: an extreme gradient boosting approach. International Review of Economics and Finance, 61, 304-323. doi: 10.101 6/j.iref.2018.03.008
Chen, T., & Guestrin, C. (2016). XGBoost: a scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, United States of América.
Dubey, A., Kumar, A., Abd-Allah, E. F., Hashem, A., & Khan, M. L. (2019). Growing more with less: breeding and developing drought resilient soybean to improve food security. Ecological Indicators, 105, 425-437. doi: 10.1016/j.ecolind.2018.03.003
Falk, K. G., Jubery, T. Z., Mirnezami, S. V., Parmley, K. A., Sarkar, S., & Singh, A.,… Singh, A. K. (2020). Computer vision and machine learning enabled soybean root phenotyping pipeline. Plant Methods, 16(1), 1-19. doi: 10.1186/s13007-019-0550-5
Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X.,… Xiang, Y. (2018). Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Conversion and Management, 164, 102-111. doi: 10.1016/j.enconman.2018.02.087
Fenta, B., Beebe, S., Kunert, K., Burridge, J., Barlow, K., Lynch, J., & Foyer, C. (2014). Field phenotyping of soybean roots for drought stress tolerance. Agronomy, 4(3), 418-435. doi: 10.3390/agronomy4030418
García-Laencina, P. J., Sancho-Gómez, J. L., & Figueiras-Vidal, A. R. (2013). Classifying patterns with missing values using Multi-Task Learning perceptrons. Expert Systems with Applications, 40(4), 1333-1341. doi: 10.1016/j.eswa.2012.08.057
Goncalves, A., Ray, P., Soper, B., Widemann, D., Nygard, M., Nygård, J. F., & Sales, A. P. (2019). Bayesian multitask learning regression for heterogeneous patient cohorts. Journal of Biomedical Informatics, 100, (Suppl.), 100059. doi: 10.1016/j.yjbinx.2019.100059
Hasson, U., Nastase, S. A., & Goldstein, A. (2020). Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron, 105(3), 416-434. doi: 10.1016/j.neuron.2019.12.002
Ito, K., Tanakamaru, K., Morita, S., Abe, J., & Inanaga, S. (2006). Lateral root development, including responses to soil drying, of maize (Zea mays) and wheat (Triticum aestivum) seminal roots. Physiologia Plantarum, 127(2), 260-267. doi: 10.1111/j.1399-3054.2006.00657.x
Iyer-Pascuzzi, A. S., Symonova, O., Mileyko, Y., Hao, Y., Belcher, H., Harer, J.,... Benfey, P. N. (2010). Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiology, 152(3), 1148-1157. doi: 10.1104/pp.109.150748
Izquierdo-Verdiguier, E., & Zurita-Milla, R. (2020). An evaluation of guided regularized random forest for classification and regression tasks in remote sensing. International Journal of Applied Earth Observation and Geoinformation, 88, 102051. doi: 10.1016/j.jag.2020.102051
Karandish, F., & Shahnazari, A. (2016). Soil temperature and maize nitrogen uptake improvement under partial root-zone drying irrigation. Pedosphere, 26(6), 872-886. doi: 10.1016/S1002-0160(15)60092-3
Krishnan, P., Singh, R., Verma, A. P. S., Joshi, D. K., & Singh, S. (2014). Changes in seed water status as characterized by NMR in developing soybean seed grown under moisture stress conditions. Biochemical and Biophysical Research Communications, 444(4), 485-490. doi: 10.1016/j.bbrc.2014.01.091
Laurett, L., Fernandes, A. A., Schmildt, E. R., Almeida, C. P., & Pinto, M. L. P. B. (2017). Desempenho da alface e da rúcula em diferentes concentrações de ferro na solução nutritiva. Revista de Ciências Agrarias - Amazon Journal of Agricultural and Environmental Sciences, 60(1), 45-52.
Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J., & Liu, J. (2020). Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model. Journal of Hydrology, 58, 124901. doi: 10.1016/j.jhydrol.2020.124901
Park, C., Kim, Y., Park, Y., & Kim, S. B. (2018). Multitask learning for virtual metrology in semiconductor manufacturing systems. Computers and Industrial Engineering, 123, 209-219. doi: 10.1016/j.cie.2018. 06.024
Patil, A. P., & Deka, P. C. (2016). An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs. Computers and Electronics in Agriculture, 121, 385-392. doi: 10.1016/j.compag. 2016.01.016
Puspasari, R., Hashiguchi, M., Ushio, R., Ishigaki, G., Tanaka, H., & Akashi, R. (2020). Evaluation of root traits in F2-progeny of interspecific hybrid between Lotus corniculatus Super-Root and tetraploid Lotus japonicus. Plant and Soil, 446(1-2), 613-625. doi: 10.1007/s11104-019-04332-2
Raghavendra, S., & Deka, P. C. (2014). Support vector machine applications in the field of hydrology: a review. Applied Soft Computing Journal, 19, 372-386. doi: 10.1016/j.asoc.2014.02.002
Rahmati, O., Falah, F., Dayal, K. S., Deo, R. C., Mohammadi, F., Biggs, T.,… Bui, D. T. (2020). Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Science of the Total Environment, 699, 134230. doi: 10.1016/j.scitotenv.2019.134230
Ratke, R. F., Santos, J. D. D. G. dos, & Souza, J. G. P. de. (2019). Métodos para estudo da dinâmica de raízes. In A. M., Zuffo, J. G. Aguilera, R. de & Oliveira (Orgs.), Ciência em Foco (Cap. 11, pp. 120-137). Nova Xavantina, MT: Pantanal Editora.
Ruiming, F., & Shijie, S. (2020). Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network. Agricultural Water Management, 236, 106177. doi: 10.1016/j.agwat.2020.106177
Sandhu, N., Anitha Raman, K., Torres, R. O., Audebert, A., Dardou, A., Kumar, A., & Henry, A. (2016). Rice root architectural plasticity traits and genetic regions for adaptability to variable cultivation and stress conditions. Plant Physiology, 171(4), 2562-2576. doi: 10.1104/pp.16.00705
Santos Silva, P. P. dos, Sousa, M. B. e, & Oliveira, E. J. de. (2019). Prediction models and selection of agronomic and physiological traits for tolerance to water deficit in cassava. Euphytica, 215(4), 1-18. doi: 10.1007/s10681-019-2399-0
Sariyer, G., Ocal Tasar, C., & Cepe, G. E. (2019). Use of data mining techniques to classify length of stay of emergency department patients. Bio-Algorithms and Med-Systems, 15(1), 20180044. doi: 10.1515/bams-2018-0044
Saruta, K., Hirai, Y., Tanaka, K., Inoue, E., Okayasu, T., & Mitsuoka, M. (2013). Predictive models for yield and protein content of brown rice using support vector machine. Computers and Electronics in Agriculture, 99, 93-100. doi: 10.1016/j.compag.2013.09.003
Singh, D., Sisodia, D. S., & Singh, P. (2020). Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease. Journal of Biomedical Informatics, 102, 103376. doi: 10.1016/j.jbi.2020.103376
Strock, C. F., De La Riva, L. M., & Lynch, J. P. (2018). Reduction in root secondary growth as a strategy for phosphorus acquisition. Plant Physiology, 176(1), 691-703. doi: 10.1104/pp.17.01583
Sun, N., Liu, C., Mei, X., Jiang, D., Wang, X., Dong, E..., & Cai, Y. (2020). QTL identification in backcross population for brace-root-related traits in maize. Euphytica, 216(2), 32. doi: 10.1007/s10681-020-2561-8
Tang, F. H., Chan, J. L. C., & Chan, B. K. L. (2019). Accurate age determination for adolescents using magnetic resonance imaging of the hand and wrist with an artificial neural network-based approach. Journal of Digital Imaging, 32(2), 283-289. doi: 10.1007/s10278-018-0135-2
Wang, M. B., & Zhang, Q. (2009). Issues in using the WinRHIZO system to determine physical characteristics of plant fine roots. Shengtai Xuebao/ Acta Ecologica Sinica, 29(2), 136-138. doi: 10.1016/j.chnaes.2009. 05.007
Zhang, L., Traore, S., Ge, J., Li, Y., Wang, S., Zhu, G.,… Fipps, G. (2019). Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel. Computers and Electronics in Agriculture, 166, 105031. doi: 10.1016/j.compag.2019.105031
Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2020). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12, 469-477. doi: 10.1016/j.gsf.2020.03.007
Zhong, D., Novais, J., Grift, T. E., Bohn, M., & Han, J. (2009). Maize root complexity analysis using a support vector machine method. Computers and Electronics in Agriculture, 69(1), 46-50. doi: 10.1016/j.compag. 2009.06.013
Zhong, R., Johnson, R., & Chen, Z. (2020). Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting (XGBoost). International Journal of Coal Geology, 220, 103416. doi: 10.1016/j.coal.2020.103416
Bressan, T. S., Souza, K. M., Girelli, T. J., Chemale, F. Jr. (2020). Evaluation of machine learning methods for lithology classification using geophysical data. Computers and Geosciences, 139, 104475. doi: 10.10 16/j.cageo.2020.104475
Carmona, P., Climent, F., & Momparler, A. (2019). Predicting failure in the U.S. banking sector: an extreme gradient boosting approach. International Review of Economics and Finance, 61, 304-323. doi: 10.101 6/j.iref.2018.03.008
Chen, T., & Guestrin, C. (2016). XGBoost: a scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, United States of América.
Dubey, A., Kumar, A., Abd-Allah, E. F., Hashem, A., & Khan, M. L. (2019). Growing more with less: breeding and developing drought resilient soybean to improve food security. Ecological Indicators, 105, 425-437. doi: 10.1016/j.ecolind.2018.03.003
Falk, K. G., Jubery, T. Z., Mirnezami, S. V., Parmley, K. A., Sarkar, S., & Singh, A.,… Singh, A. K. (2020). Computer vision and machine learning enabled soybean root phenotyping pipeline. Plant Methods, 16(1), 1-19. doi: 10.1186/s13007-019-0550-5
Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X.,… Xiang, Y. (2018). Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Conversion and Management, 164, 102-111. doi: 10.1016/j.enconman.2018.02.087
Fenta, B., Beebe, S., Kunert, K., Burridge, J., Barlow, K., Lynch, J., & Foyer, C. (2014). Field phenotyping of soybean roots for drought stress tolerance. Agronomy, 4(3), 418-435. doi: 10.3390/agronomy4030418
García-Laencina, P. J., Sancho-Gómez, J. L., & Figueiras-Vidal, A. R. (2013). Classifying patterns with missing values using Multi-Task Learning perceptrons. Expert Systems with Applications, 40(4), 1333-1341. doi: 10.1016/j.eswa.2012.08.057
Goncalves, A., Ray, P., Soper, B., Widemann, D., Nygard, M., Nygård, J. F., & Sales, A. P. (2019). Bayesian multitask learning regression for heterogeneous patient cohorts. Journal of Biomedical Informatics, 100, (Suppl.), 100059. doi: 10.1016/j.yjbinx.2019.100059
Hasson, U., Nastase, S. A., & Goldstein, A. (2020). Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron, 105(3), 416-434. doi: 10.1016/j.neuron.2019.12.002
Ito, K., Tanakamaru, K., Morita, S., Abe, J., & Inanaga, S. (2006). Lateral root development, including responses to soil drying, of maize (Zea mays) and wheat (Triticum aestivum) seminal roots. Physiologia Plantarum, 127(2), 260-267. doi: 10.1111/j.1399-3054.2006.00657.x
Iyer-Pascuzzi, A. S., Symonova, O., Mileyko, Y., Hao, Y., Belcher, H., Harer, J.,... Benfey, P. N. (2010). Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiology, 152(3), 1148-1157. doi: 10.1104/pp.109.150748
Izquierdo-Verdiguier, E., & Zurita-Milla, R. (2020). An evaluation of guided regularized random forest for classification and regression tasks in remote sensing. International Journal of Applied Earth Observation and Geoinformation, 88, 102051. doi: 10.1016/j.jag.2020.102051
Karandish, F., & Shahnazari, A. (2016). Soil temperature and maize nitrogen uptake improvement under partial root-zone drying irrigation. Pedosphere, 26(6), 872-886. doi: 10.1016/S1002-0160(15)60092-3
Krishnan, P., Singh, R., Verma, A. P. S., Joshi, D. K., & Singh, S. (2014). Changes in seed water status as characterized by NMR in developing soybean seed grown under moisture stress conditions. Biochemical and Biophysical Research Communications, 444(4), 485-490. doi: 10.1016/j.bbrc.2014.01.091
Laurett, L., Fernandes, A. A., Schmildt, E. R., Almeida, C. P., & Pinto, M. L. P. B. (2017). Desempenho da alface e da rúcula em diferentes concentrações de ferro na solução nutritiva. Revista de Ciências Agrarias - Amazon Journal of Agricultural and Environmental Sciences, 60(1), 45-52.
Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J., & Liu, J. (2020). Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model. Journal of Hydrology, 58, 124901. doi: 10.1016/j.jhydrol.2020.124901
Park, C., Kim, Y., Park, Y., & Kim, S. B. (2018). Multitask learning for virtual metrology in semiconductor manufacturing systems. Computers and Industrial Engineering, 123, 209-219. doi: 10.1016/j.cie.2018. 06.024
Patil, A. P., & Deka, P. C. (2016). An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs. Computers and Electronics in Agriculture, 121, 385-392. doi: 10.1016/j.compag. 2016.01.016
Puspasari, R., Hashiguchi, M., Ushio, R., Ishigaki, G., Tanaka, H., & Akashi, R. (2020). Evaluation of root traits in F2-progeny of interspecific hybrid between Lotus corniculatus Super-Root and tetraploid Lotus japonicus. Plant and Soil, 446(1-2), 613-625. doi: 10.1007/s11104-019-04332-2
Raghavendra, S., & Deka, P. C. (2014). Support vector machine applications in the field of hydrology: a review. Applied Soft Computing Journal, 19, 372-386. doi: 10.1016/j.asoc.2014.02.002
Rahmati, O., Falah, F., Dayal, K. S., Deo, R. C., Mohammadi, F., Biggs, T.,… Bui, D. T. (2020). Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Science of the Total Environment, 699, 134230. doi: 10.1016/j.scitotenv.2019.134230
Ratke, R. F., Santos, J. D. D. G. dos, & Souza, J. G. P. de. (2019). Métodos para estudo da dinâmica de raízes. In A. M., Zuffo, J. G. Aguilera, R. de & Oliveira (Orgs.), Ciência em Foco (Cap. 11, pp. 120-137). Nova Xavantina, MT: Pantanal Editora.
Ruiming, F., & Shijie, S. (2020). Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network. Agricultural Water Management, 236, 106177. doi: 10.1016/j.agwat.2020.106177
Sandhu, N., Anitha Raman, K., Torres, R. O., Audebert, A., Dardou, A., Kumar, A., & Henry, A. (2016). Rice root architectural plasticity traits and genetic regions for adaptability to variable cultivation and stress conditions. Plant Physiology, 171(4), 2562-2576. doi: 10.1104/pp.16.00705
Santos Silva, P. P. dos, Sousa, M. B. e, & Oliveira, E. J. de. (2019). Prediction models and selection of agronomic and physiological traits for tolerance to water deficit in cassava. Euphytica, 215(4), 1-18. doi: 10.1007/s10681-019-2399-0
Sariyer, G., Ocal Tasar, C., & Cepe, G. E. (2019). Use of data mining techniques to classify length of stay of emergency department patients. Bio-Algorithms and Med-Systems, 15(1), 20180044. doi: 10.1515/bams-2018-0044
Saruta, K., Hirai, Y., Tanaka, K., Inoue, E., Okayasu, T., & Mitsuoka, M. (2013). Predictive models for yield and protein content of brown rice using support vector machine. Computers and Electronics in Agriculture, 99, 93-100. doi: 10.1016/j.compag.2013.09.003
Singh, D., Sisodia, D. S., & Singh, P. (2020). Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease. Journal of Biomedical Informatics, 102, 103376. doi: 10.1016/j.jbi.2020.103376
Strock, C. F., De La Riva, L. M., & Lynch, J. P. (2018). Reduction in root secondary growth as a strategy for phosphorus acquisition. Plant Physiology, 176(1), 691-703. doi: 10.1104/pp.17.01583
Sun, N., Liu, C., Mei, X., Jiang, D., Wang, X., Dong, E..., & Cai, Y. (2020). QTL identification in backcross population for brace-root-related traits in maize. Euphytica, 216(2), 32. doi: 10.1007/s10681-020-2561-8
Tang, F. H., Chan, J. L. C., & Chan, B. K. L. (2019). Accurate age determination for adolescents using magnetic resonance imaging of the hand and wrist with an artificial neural network-based approach. Journal of Digital Imaging, 32(2), 283-289. doi: 10.1007/s10278-018-0135-2
Wang, M. B., & Zhang, Q. (2009). Issues in using the WinRHIZO system to determine physical characteristics of plant fine roots. Shengtai Xuebao/ Acta Ecologica Sinica, 29(2), 136-138. doi: 10.1016/j.chnaes.2009. 05.007
Zhang, L., Traore, S., Ge, J., Li, Y., Wang, S., Zhu, G.,… Fipps, G. (2019). Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel. Computers and Electronics in Agriculture, 166, 105031. doi: 10.1016/j.compag.2019.105031
Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2020). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12, 469-477. doi: 10.1016/j.gsf.2020.03.007
Zhong, D., Novais, J., Grift, T. E., Bohn, M., & Han, J. (2009). Maize root complexity analysis using a support vector machine method. Computers and Electronics in Agriculture, 69(1), 46-50. doi: 10.1016/j.compag. 2009.06.013
Zhong, R., Johnson, R., & Chen, Z. (2020). Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting (XGBoost). International Journal of Coal Geology, 220, 103416. doi: 10.1016/j.coal.2020.103416
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2022-02-28
Como Citar
Duarte, A. B., Ferreira, D. de O., Ferreria, L. B., & Silva, F. L. da. (2022). Machine learning aplicado à predição da arquitetura radicular de cultivares de soja sob duas condições de disponibilidade hídrica. Semina: Ciências Agrárias, 43(3), 1017–1036. https://doi.org/10.5433/1679-0359.2022v43n3p1017
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