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Agronomy 2019, 9(2), 72; https://doi.org/10.3390/agronomy9020072

LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

1
Department of Electrical & Computer Engineering, COMSATS University Islamabad, G.T. Road, Wah Cantonment 47040, Pakistan
2
Department of Computer Science & Information Technology, Ghazi University, D.G. Khan 32200, Pakistan
3
Department of Electrical & Computer Engineering, COMSATS University Islamabad, College Road, Tobe Camp, Abbottabad 22060, Pakistan
4
Department of Mathematics, COMSATS University Islamabad, G.T. Road, Wah Cantonment 47040, Pakistan
*
Author to whom correspondence should be addressed.
Received: 11 January 2019 / Revised: 28 January 2019 / Accepted: 29 January 2019 / Published: 8 February 2019
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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Abstract

Pakistan’s economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, thereby, threatening the country’s economy in the years to come. This work focuses on developing an accurate wheat production forecasting model using the Long Short Term Memory (LSTM) neural networks, which are considered to be highly accurate for time series prediction. A data pre-processing smoothing mechanism, in conjunction with the LSTM based model, is used to further improve the prediction accuracy. A comparison of the proposed mechanism with a few existing models in literature is also given. The results verify that the proposed model achieves better performance in terms of forecasting, and reveal that while the wheat production will gradually increase in the next ten years, the production to consumption ratio will continue to fall and pose threats to the overall economy. Our proposed framework, therefore, may be used as guidelines for wheat production in particular, and is amenable to other crops as well, leading to sustainable agriculture development in general. View Full-Text
Keywords: wheat production; time series forecasting; long short term memory neural networks; smoothing function wheat production; time series forecasting; long short term memory neural networks; smoothing function
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Haider, S.A.; Naqvi, S.R.; Akram, T.; Umar, G.A.; Shahzad, A.; Sial, M.R.; Khaliq, S.; Kamran, M. LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan. Agronomy 2019, 9, 72.

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