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LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan
| Content Provider | Semantic Scholar |
|---|---|
| Author | Haider, Sajjad Naqvi, Syed Rameez Akram, Tallha Umar, Gulfam Ahmad Shahzad, Aamir Sial, Muhammad Rafiq Khaliq, Shoaib Kamran, Muhammad |
| Copyright Year | 2019 |
| 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. |
| Starting Page | 72 |
| Ending Page | 72 |
| Page Count | 1 |
| File Format | PDF HTM / HTML |
| DOI | 10.3390/agronomy9020072 |
| Volume Number | 9 |
| Alternate Webpage(s) | https://res.mdpi.com/d_attachment/agronomy/agronomy-09-00072/article_deploy/agronomy-09-00072-v2.pdf |
| Alternate Webpage(s) | https://doi.org/10.3390/agronomy9020072 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
