Maize Yield Prediction using Artificial Neural Networks based on a Trial Network Dataset
Received: 6 January 2023 | Revised: 17 January 2023 | Accepted: 20 January 2023 | Online: 2 April 2023
Corresponding author: Glauco Vieira Miranda
Abstract
The prediction of grain yield is important for sowing, cultivar positioning, crop management, and public policy. This study aims to predict maize productivity by applying an artificial neural network and by building models of multilayer perceptrons (MLPs) using public data and maize experimental networks. The dataset included parameters of climate, soil water balance, and agronomic characteristics from maize hybrids of an experimental network of two agricultural years. The climatic and soil balance water parameters were divided according to the maize plant development stages. Six databases were obtained by combining the imputation of missing data with the agronomic characteristics of the maize hybrids, the climatic parameters/soil water balance, and the complete database with both. Hyper parameterization of the models was obtained using GridSearch and k-fold cross-validation. The models with imputation were more accurate than those without it. The model with climate data/soil water balance and the complete model with imputation presented the smallest errors of 71 kg ha−1. In all the models, cultivars, locations, and their interactions were important, and different climatic conditions had the greatest weight in predicting productivity. It was concluded that the MLP models performed adequately and captured the non-linear effects of the interaction between the environment and maize hybrids. Climatic and soil balance water parameters at different stages of maize plant development explain the productivity of maize hybrids more than the agronomic characteristics of the cultivars.
Keywords:
deep learning, artificial neural networks, multilayer perceptron, agricultular productivityDownloads
References
A. Singamsetti et al., "Genotype × environment interaction and selection of maize (Zea mays L.) hybrids across moisture regimes," Field Crops Research, vol. 270, Aug. 2021, Art. no. 108224. DOI: https://doi.org/10.1016/j.fcr.2021.108224
N. Anuradha et al., "Comparative Study of AMMI- and BLUP-Based Simultaneous Selection for Grain Yield and Stability of Finger Millet [Eleusine coracana (L.) Gaertn.] Genotypes," Frontiers in plant science, vol. 12, Jan. 2021, Art. no. 786839. DOI: https://doi.org/10.3389/fpls.2021.786839
M. Balderacchi et al., "Genotype by Environment Interaction on Tropical Maize Hybrids Under Normal Irrigation and Waterlogging Conditions," Frontiers in Sustainable Food Systems, vol. 6, Jun. 2022, Art. no. 913211. DOI: https://doi.org/10.3389/fsufs.2022.913211
M. Abdelrahman et al., "Detection of Superior Rice Genotypes and Yield Stability under Different Nitrogen Levels Using AMMI Model and Stability Statistics," Plants, vol. 11, no. 20, Jan. 2022, Art. no. 2775. DOI: https://doi.org/10.3390/plants11202775
L. V. de Souza, G. V. Miranda, J. C. C. Galvao, L. J. M. Guimaraes, and I. C. dos Santos, "Combining ability of maize grain yield under different levels of environmental stress," Pesquisa Agropecuária Brasileira, vol. 44, pp. 1297–1303, Oct. 2009. DOI: https://doi.org/10.1590/S0100-204X2009001000013
N. C. Eli-Chukwu, "Applications of Artificial Intelligence in Agriculture: A Review," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4377–4383, Aug. 2019. DOI: https://doi.org/10.48084/etasr.2756
"Zoneamento Agricola," Ministerio da Agricultura e Pecuaria. https://www.gov.br/agricultura/pt-br/assuntos/riscos-seguro/programa-nacional-de-zoneamento-agricola-de-risco-climatico/zoneamento-agricola.
State of the Climate in Latin America and the Caribbean 2021 (WMO-No. 1295). Geneva, Switzerland: WMO, 2022.
M. Kaul, R. L. Hill, and C. Walthall, "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, vol. 85, no. 1, pp. 1–18, Jul. 2005. DOI: https://doi.org/10.1016/j.agsy.2004.07.009
S. Khaki and L. Wang, "Crop Yield Prediction Using Deep Neural Networks," Frontiers in Plant Science, vol. 10, May 2019, Art. no. 621. DOI: https://doi.org/10.3389/fpls.2019.00621
J. Fan, J. Bai, Z. Li, A. Ortiz-Bobea, and C. P. Gomes, "A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 11, pp. 11873–11881, Jun. 2022. DOI: https://doi.org/10.1609/aaai.v36i11.21444
A. Etminan, A. Pour-Aboughadareh, R. Mohammadi, L. Shooshtari, M. Yousefiazarkhanian, and H. Moradkhani, "Determining the best drought tolerance indices using Artificial Neural Network (ANN): Insight into application of intelligent agriculture in agronomy and plant breeding," Cereal Research Communications, vol. 47, no. 1, pp. 170–181, Mar. 2019. DOI: https://doi.org/10.1556/0806.46.2018.057
S. Khaki, Z. Khalilzadeh, and L. Wang, "Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach," PLOS ONE, vol. 15, no. 5, 2020, Art. no. e0233382. DOI: https://doi.org/10.1371/journal.pone.0233382
L. de O. Amaral, G. V. Miranda, B. H. P. Val, A. P. Silva, A. C. R. Moitinho, and S. H. Uneda-Trevisoli, "Artificial Neural Network for Discrimination and Classification of Tropical Soybean Genotypes of Different Relative Maturity Groups," Frontiers in Plant Science, vol. 13, Jul. 2022, Art. no. 814046. DOI: https://doi.org/10.3389/fpls.2022.814046
A. H. Blasi, M. A. Abbadi, and R. Al-Huweimel, "Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6609–6613, Feb. 2021. DOI: https://doi.org/10.48084/etasr.3944
N. C. Kundur and P. B. Mallikarjuna, "Deep Convolutional Neural Network Architecture for Plant Seedling Classification," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9464–9470, Dec. 2022. DOI: https://doi.org/10.48084/etasr.5282
G. K. Michelon, P. L. de Menezes, C. L. Bazzi, E. P. Jasse, P. S. G. Magalhaes, and L. F. Borges, "Artificial neural networks to estimate the productivity of soybeans and corn by chlorophyll readings," Journal of Plant Nutrition, vol. 41, no. 10, pp. 1285–1292, Jun. 2018. DOI: https://doi.org/10.1080/01904167.2018.1447579
Y. Kittichotsatsawat, N. Tippayawong, and K. Y. Tippayawong, "Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques," Scientific Reports, vol. 12, no. 1, Aug. 2022, Art. no. 14488. DOI: https://doi.org/10.1038/s41598-022-18635-5
A. P. Duarte and E. Sawazaki, Avaliação regional de cultivares de milho safrinha Resultados 2018, 1st ed. Assis, Brazil: IAC/APTA, 2018.
A. P. Duarte and E. Sawazaki, Avaliação regional de cultivares de milho safrinha Resultados 2019, 1st ed. Assis, Brazil: IAC/APTA, 2019.
P. V. D. de Souza, "Rede neural artificial para predicao da produtividade da cultura do milho," Ph.D. dissertation, Federal University of Technology-Parana, Santa Helena, Brazil, 2021.
"BDMEP." https://bdmep.inmet.gov.br/.
M. B. Richman, T. B. Trafalis, and I. Adrianto, "Missing Data Imputation Through Machine Learning Algorithms," in Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds. Dordrecht, Netherlands: Springer, 2009, pp. 153–169. DOI: https://doi.org/10.1007/978-1-4020-9119-3_7
F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2826–2830, Oct. 2011.
E. Bisong, "More supervised machine learning techniques with Scikit-learn," in Building Machine Learning and Deep Learning Models on Google Cloud Platform, Ottawa, ON, Canada: Apress, 2019, pp. 287–308. DOI: https://doi.org/10.1007/978-1-4842-4470-8_24
"Google Colab." https://research.google.com/colaboratory/faq.html.
"Keras: Deep Learning for humans." Keras, Feb. 02, 2023, Accessed: Feb. 02, 2023. [Online]. Available: https://github.com/keras-team/keras.
M. Abadi et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems." arXiv, Mar. 16, 2016.
S. M. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," in 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA, Dec. 2017, pp. 1–10.
B. Li, "Random Search Plus: A more effective random search for machine learning hyperparameters optimization," M.S. thesis, University of Tennessee, Knoxville, TN, United States, 2020.
E. Ndiaye, T. Le, O. Fercoq, J. Salmon, and I. Takeuchi, "Safe Grid Search with Optimal Complexity," in 36th International Conference on Machine Learning, Long Beach, CA, USA, Jun. 2019, pp. 4771–4780.
M. Shahhosseini, G. Hu, and S. V. Archontoulis, "Forecasting Corn Yield With Machine Learning Ensembles," Frontiers in Plant Science, vol. 11, Jul. 2020, Art. no. 1120. DOI: https://doi.org/10.3389/fpls.2020.01120
V. Barbosa dos Santos, A. M. F. dos Santos, and G. de S. Rolim, "Estimation and forecasting of soybean yield using artificial neural networks," Agronomy Journal, vol. 113, no. 4, pp. 3193–3209, 2021. DOI: https://doi.org/10.1002/agj2.20729
R. R. Fidelis, G. V. Miranda, I. C. dos Santos, J. C. C. Galvao, J. M. Peluzio, and S. de O. Lima, "Fontes de germoplasma de milho para estresse de baixo nitrogenio," Pesquisa Agropecuaria Tropical, vol. 37, no. 3, pp. 147–153, Oct. 2007.
M. O. Soares, G. V. Miranda, L. J. M. Guimaraes, I. E. Marriel, and C. T. Guimaraes, "Parametros geneticos de uma populacao de milho em niveis contrastantes de nitrogenio," Revista Ciencia Agronomica, vol. 42, pp. 168–174, Mar. 2011. DOI: https://doi.org/10.1590/S1806-66902011000100021
I. C. D. Santos et al., "Comportamento de cultivares de milho produzidos organicamente e correlacoes entre caracteristicas das espigas colhidas no estadio verde," Revista Brasileira de Milho e Sorgo, vol. 4, no. 1, pp. 45–53, 2005. DOI: https://doi.org/10.18512/1980-6477/rbms.v4n1p45-53
G. de O. Garcia, P. A. Ferreira, G. V. Miranda, F. G. de Oliveira, and D. B. dos Santos, "Indices fisiologicos, crescimento e producao do milho irrigado com agua salina," Irriga, vol. 12, no. 3, pp. 307–325, Sep. 2007. DOI: https://doi.org/10.15809/irriga.2007v12n3p307-325
A. Borem, G. V. Miranda, and R. Fritsche-Neto, Melhoramento de plantas. Brazil, USA: Oficina de Textos, 2021.
G. V. Miranda, E. M. W. Braun, M. E. V. B. Alves, P. Machado, and A. de M. Ramos, "Desempenho de hibridos de milho em diferentes epocas de semeadura na segunda safra em baixa altitude no extremo Oeste do Estado do Parana," Brazilian Journal of Development, vol. 7, no. 4, pp. 34794–34810, Apr. 2021. DOI: https://doi.org/10.34117/bjdv7n4-100
F. R. Pires, C. M. Souza, D. M. Queiroz, G. V. Miranda, and J. C. C. Galvao, "Alteracao de atributos quimicos do solo e estado nutricional e caracteristicas agronomicas de plantas de milho, considerando as modalidades de calagem em plantio direto," Revista Brasileira de Ciencia do Solo, vol. 27, pp. 121–131, Feb. 2003. DOI: https://doi.org/10.1590/S0100-06832003000100013
S. Chakraborty, A. R. Belekar, A. Datye, and N. Sinha, "Isotopic study of intraseasonal variations of plant transpiration: an alternative means to characterise the dry phases of monsoon," Scientific Reports, vol. 8, no. 1, Jun. 2018, Art. no. 8647. DOI: https://doi.org/10.1038/s41598-018-26965-6
F. Morales et al., "Photosynthetic Metabolism under Stressful Growth Conditions as a Bases for Crop Breeding and Yield Improvement," Plants, vol. 9, no. 1, Jan. 2020, Art. no. 88. DOI: https://doi.org/10.3390/plants9010088
Y. Li, H. Tao, B. Zhang, S. Huang, and P. Wang, "Timing of Water Deficit Limits Maize Kernel Setting in Association With Changes in the Source-Flow-Sink Relationship," Frontiers in Plant Science, vol. 9, 2018, Art. no. 1326. DOI: https://doi.org/10.3389/fpls.2018.01326
J. L. Hatfield and J. H. Prueger, "Temperature extremes: Effect on plant growth and development," Weather and Climate Extremes, vol. 10, pp. 4–10, Dec. 2015. DOI: https://doi.org/10.1016/j.wace.2015.08.001
E. A. Minato, B. M. A. R. Cassim, M. R. Besen, F. L. Mazzi, T. T. Inoue, and M. A. Batista, "Controlled-release nitrogen fertilizers: characterization, ammonia volatilization, and effects on second-season corn," Revista Brasileira de Ciencia do Solo, vol. 44, May 2020, Art. no. e0190108. DOI: https://doi.org/10.36783/18069657rbcs20190108
H. A. Cleugh, J. M. Miller, and M. Bohm, "Direct mechanical effects of wind on crops," Agroforestry Systems, vol. 41, no. 1, pp. 85–112, Apr. 1998. DOI: https://doi.org/10.1023/A:1006067721039
C. C. Westhues et al., "Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks," Frontiers in Plant Science, vol. 12, 2021, Art. no. 699589. DOI: https://doi.org/10.3389/fpls.2021.699589
A. A. Chassaigne-Ricciulli, L. E. Mendoza-Onofre, L. Cordova-Tellez, A. Carballo-Carballo, F. M. San Vicente-Garcia, and T. Dhliwayo, "Effective Seed Yield and Flowering Synchrony of Parents of CIMMYT Three-Way-Cross Tropical Maize Hybrids," Agriculture, vol. 11, no. 2, Feb. 2021, Art. no. 161. DOI: https://doi.org/10.3390/agriculture11020161
R. Fritsche-Neto, R. A. Vieira, C. A. Scapim, G. V. Miranda, and L. M. Rezende, "Updating the ranking of the coefficients of variation from maize experiments," Acta Scientiarum. Agronomy, vol. 34, pp. 99–101, Mar. 2012. DOI: https://doi.org/10.4025/actasciagron.v34i1.13115
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Copyright (c) 2023 Paulo Vitor Duarte de Souza, Leiliane Pereira de Rezende, Aildson Pereira Duarte, Glauco Miranda
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