Convolutional and Spiking Neural Network Models for Crop Yield Forecasting

G. Karuna; K. Pravallika; K. Anuradha; V. Srilakshmi

doi:10.1051/e3sconf/202130901162

All issues

Volume 309 (2021)

E3S Web Conf., 309 (2021) 01162

References

Open Access

Issue		E3S Web Conf. Volume 309, 2021 3^rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)


Article Number		01162
Number of page(s)		11
DOI		https://doi.org/10.1051/e3sconf/202130901162
Published online		07 October 2021

Khaki, Saeed & Wang, Lizhi. (2019). Crop Yield Prediction Using Deep Neural Networks. Frontiers in Plant Science.10.10.3389/fpls.2019.00621. [Google Scholar]
Syngenta (2021). Syngenta Crop Challenge In Analytics. Available online at: https://www.ideaconnection.com/syngenta-crop-challenge/challenge.php/ [Google Scholar]
Elizondo, David & McClendon, R.W. (1994). Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean. Transactions of the American Society of Agricultural Engineers. 37. 981–988. 10.13031/2013.28168. [Google Scholar]
Matsumura, K. & Gaitan, Carlos & Sugimoto, K. & Cannon, Alex & Hsieh, William. (2015). Maize yield forecasting by linear regression and artificial neural networks in Jilin, China.1-12. [Google Scholar]
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Baum, Mitch & Archontoulis, S. &Licht, Mark. (2018). Planting Date, Hybrid Maturity, and Weather Effects on Maize Yield and Crop Stage. Agronomy Journal.111. 10.2134/agronj2018.04.0297. [Google Scholar]
AndreasKamilaris, Francesc X. Prenafeta-Boldú, Deep learning in agriculture: A survey, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2018.02.016. (https://www.sciencedirect.com/science/article/pii/S0168169917308803) [Google Scholar]
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Shahhosseini, Mohsen & Hu, Guiping& Huber, Isaiah & Archontoulis, Sotirios. (2021). Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Scientific Reports. 11. 10.1038/s41598-020-80820-1. [PubMed] [Google Scholar]
Mulla, Sadiq&Quadri, S..(2020). Crop-yield and Price Forecasting using Machine Learning.TheInternational journal of analytical and experimental modal analysis. XII. 1731-1737. [Google Scholar]
T. Siddique, D. Barua, Z. Ferdous and A. Chakrabarty, “Automated farming prediction, “ 2017 Intelligent Systems Conference (IntelliSys), 2017, pp. 757–763, doi: 10.1109/IntelliSys.2017.8324214. [Google Scholar]
Dharani, M &Thamilselvan, R & Natesan, P & Kalaivaani, PCD &Santhoshkumar, S. (2021). Review on Crop Prediction Using Deep Learning Techniques. Journal of Physics: Conference Series. 1767. 012026. 10.1088/1742-6596/1767/1/012026. [Google Scholar]

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[1] Khaki, Saeed & Wang, Lizhi. (2019). Crop Yield Prediction Using Deep Neural Networks. Frontiers in Plant Science.10.10.3389/fpls.2019.00621. [Google Scholar]

[2] Syngenta (2021). Syngenta Crop Challenge In Analytics. Available online at: https://www.ideaconnection.com/syngenta-crop-challenge/challenge.php/ [Google Scholar]

[3] Elizondo, David & McClendon, R.W. (1994). Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean. Transactions of the American Society of Agricultural Engineers. 37. 981–988. 10.13031/2013.28168. [Google Scholar]

[4] Matsumura, K. & Gaitan, Carlos & Sugimoto, K. & Cannon, Alex & Hsieh, William. (2015). Maize yield forecasting by linear regression and artificial neural networks in Jilin, China.1-12. [Google Scholar]

[5] PayalGulati, Suman Kumar Jha, 2020, Efficient Crop Yield Prediction in India using Machine Learning Techniques, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ENCADEMS – 2020 (Volume 8 – Issue 10), [Google Scholar]

[6] Barbosa, Alexandre & Trevisan, Rodrigo & Hovakimyan, Naira & Martin, Nicolas. (2020). Modeling yield response to crop management using convolutional neural networks. Computers and Electronics in Agriculture. 170. 105197. 10.1016/j.compag.2019.105197. [Google Scholar]

[7] Baum, Mitch & Archontoulis, S. &Licht, Mark. (2018). Planting Date, Hybrid Maturity, and Weather Effects on Maize Yield and Crop Stage. Agronomy Journal.111. 10.2134/agronj2018.04.0297. [Google Scholar]

[8] AndreasKamilaris, Francesc X. Prenafeta-Boldú, Deep learning in agriculture: A survey, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2018.02.016. (https://www.sciencedirect.com/science/article/pii/S0168169917308803) [Google Scholar]

[9] Khaki, Saeed& Wang, Lizhi&Archontoulis, Sotirios. (2019). A CNN-RNN Framework for Crop Yield Prediction. [Google Scholar]

[10] Shahhosseini, Mohsen & Hu, Guiping& Huber, Isaiah & Archontoulis, Sotirios. (2021). Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Scientific Reports. 11. 10.1038/s41598-020-80820-1. [PubMed] [Google Scholar]

[11] Mulla, Sadiq&Quadri, S..(2020). Crop-yield and Price Forecasting using Machine Learning.TheInternational journal of analytical and experimental modal analysis. XII. 1731-1737. [Google Scholar]

[12] T. Siddique, D. Barua, Z. Ferdous and A. Chakrabarty, “Automated farming prediction, “ 2017 Intelligent Systems Conference (IntelliSys), 2017, pp. 757–763, doi: 10.1109/IntelliSys.2017.8324214. [Google Scholar]

[13] Dharani, M &Thamilselvan, R & Natesan, P & Kalaivaani, PCD &Santhoshkumar, S. (2021). Review on Crop Prediction Using Deep Learning Techniques. Journal of Physics: Conference Series. 1767. 012026. 10.1088/1742-6596/1767/1/012026. [Google Scholar]