RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING

Year 2022, , 204 - 218, 14.04.2022

Halit Çetiner , Burhan Kara

https://doi.org/10.54365/adyumbd.1075265

Cited By: 9

Abstract

In the study carried out in line with the stated purposes, monthly rain, humidity and temperature data, wheat production amount, and wheat productivity data of Konya province between 1980-2020 were used. Using these data, wheat productivity estimation was performed with (Gated Recurrent Units) GRU and Long Short Term Memory (LSTM) methods, which are Recurrent Neural Network (RNN) based algorithms. When wheat productivity estimation performance was examined with the implemented GRU-based model, 0.9550, 0.0059, 0.0280, 0.0623, 7.45 values were obtained for the R2 score, MSE, RMSE, MAE and MAPE values, respectively. In the performance results obtained with the LSTM method, which is another RNN-based method, 0.9667, 0.0054, 0.0280, 0.0614, 7.33 values were obtained for the R2 score, MSE, RMSE, MAE and MAPE values, respectively. Although the LSTM method gave better results than the GRU method, the training modelling time of the LSTM method took longer than that of the GRU method.

Keywords

Wheat yield, wheat production, GRU, LSTM, regression analysis

BUĞDAY VERİM TAHMİNİ İÇİN YENİLEMELİ SİNİR AĞI TABANLI MODEL GELİŞTİRME

Abstract

Bu çalışmada 1980-2020 yılları arasında Konya ilinin aylık yağış, nem ve sıcaklık verileri, buğday üretim miktarı ve buğday verimlilik verileri kullanılmıştır. Bu veriler kullanılarak Recurrent Neural Network (RNN) tabanlı algoritmalar olan (Gated Recurrent Units) GRU ve Long Short Term Memory (LSTM) yöntemleri ile buğday verimlilik tahmini yapılmıştır. Gerçekleştirilen GRU tabanlı model ile buğday verimliliği tahmin performansları incelendiğinde R2 puan, MSE, RMSE, MAE ve MAPE değerleri için sırasıyla 0.9550, 0.0059, 0.0280, 0.0623, 7.45 değerleri elde edilmiştir. RNN tabanlı bir diğer yöntem olan LSTM yöntemiyle elde edilen performans sonuçlarında ise R2 puan, MSE, RMSE, MAE ve MAPE değerleri için sırasıyla 0.9667, 0.0054, 0.0280, 0.0614, 7.33 değerleri elde edilmiştir. LSTM yöntemi, GRU yönteminden daha iyi sonuçlar vermesine rağmen LSTM yönteminin eğitim modelleme süresi GRU yönteminden daha fazla sürmüştür.

Keywords

Buğday verimi, buğday üretimi, GRU, LSTM, regresyon analizi

References

• Vanli, Ö., Ustundag, B. B., Ahmad, I., Hernandez-Ochoa, I. M., & Hoogenboom, G. (2019). Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey. Environmental Science and Pollution Research, 26(28), 29397–29408. https://doi.org/10.1007/s11356-019-06061-6.
• Asseng, S., Cammarano, D., Basso, B., Chung, U., Alderman, P. D., Sonder, K., … Lobell, D. B. (2017). Hot spots of wheat yield decline with rising temperatures. Global Change Biology, 23(6), 2464–2472. https://doi.org/https://doi.org/10.1111/gcb.13530.
• Cao, J., Zhang, Z., Luo, Y., Zhang, L., Zhang, J., Li, Z., & Tao, F. (2021). Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. European Journal of Agronomy, 123, 126204. https://doi.org/https://doi.org/10.1016/j.eja.2020.126204.
• FAO, I. (2017). WFP (2015). The state of food insecurity in the World. Meeting the 2015 international hunger targets: taking stock of uneven progress. Rome, FAO.
• Dodds, F., & Bartram, J. (2016). The water, food, energy, and climate Nexus: Challenges and an agenda for action. Routledge.
• Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/https://doi.org/10.1016/j.rse.2017.06.031.
• Vanli, Ö., Ahmad, I., & Ustundag, B. B. (2020). Area Estimation and Yield Forecasting of Wheat in Southeastern Turkey Using a Machine Learning Approach. Journal of the Indian Society of Remote Sensing, 48(12), 1757–1766. https://doi.org/10.1007/s12524-020-01196-3.
• He, Z., Xia, X., & Zhang, Y. (2010). Breeding Noodle Wheat in China. In Asian Noodles: Science, Technology, and Processing (pp. 1–23). https://doi.org/10.1002/9780470634370.ch1.
• Chen, Y., Zhang, Z., Tao, F., Wang, P., & Wei, X. (2017). Spatio-temporal patterns of winter wheat yield potential and yield gap during the past three decades in North China. Field Crops Research, 206, 11–20. https://doi.org/https://doi.org/10.1016/j.fcr.2017.02.012.
• Ahmad, I., Saeed, U., Fahad, M., Ullah, A., Habib ur Rahman, M., Ahmad, A., & Judge, J. (2018). Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing, 46(10), 1701–1711. https://doi.org/10.1007/s12524-018-0825-8.
• Nasim, W., Amin, A., Fahad, S., Awais, M., Khan, N., Mubeen, M., … Jamal, Y. (2018). Future risk assessment by estimating historical heat wave trends with projected heat accumulation using SimCLIM climate model in Pakistan. Atmospheric Research, 205, 118–133. https://doi.org/https://doi.org/10.1016/j.atmosres.2018.01.009.
• Ben-Asher, J., Yano, T., Aydın, M., & Garcia y Garcia, A. (2019). Enhanced Growth Rate and Reduced Water Demand of Crop Due to Climate Change in the Eastern Mediterranean Region (pp. 269–293). https://doi.org/10.1007/978-3-030-01036-2_13.
• Ahmad, I., Wajid, S. A., Ahmad, A., Cheema, M. J. M., & Judge, J. (2019). Optimizing irrigation and nitrogen requirements for maize through empirical modeling in semi-arid environment. Environmental Science and Pollution Research, 26(2), 1227–1237. https://doi.org/10.1007/s11356-018-2772-x.
• Belhouchette, H., Nasim, W., Shahzada, T., Hussain, A., Therond, O., Fahad, E., … Wery, J. (2017). Economic and environmental impacts of introducing grain legumes in farming systems of Midi-Pyrenees region (France): a simulation approach.
• Dogan, H. G., & Karakas, G. (2018). The effect of climatic factors on wheat yield in Turkey: a panel DOLS approach. Fresenius Environ Bull, 27, 4162–4168.
• Dudu, H., & Cakmak, E. H. (2018). Climate change and agriculture: an integrated approach to evaluate economy-wide effects for Turkey. Climate and Development, 10(3), 275–288.
• TÜİK. (2021). TÜİK. Retrieved from https://data.tuik.gov.tr/.
• Cline, W. R. (2007). Global warming and agriculture: End-of-century estimates by country. Peterson Institute.
• Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., … Ciais, P. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 114(35), 9326–9331.
• Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., … White, J. W. (2015). Rising temperatures reduce global wheat production. Nature climate change, 5(2), 143–147.
• Ahmed, I., Ullah, A., Rahman, M. H. ur, Ahmad, B., Wajid, S. A., Ahmad, A., & Ahmed, S. (2019). Climate change impacts and adaptation strategies for agronomic crops. In Climate change and agriculture (pp. 1–14). IntechOpen London, UK.
• Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., … Kim, S.-H. (2016). Random Forests for Global and Regional Crop Yield Predictions. PLOS ONE, 11(6), e0156571. Retrieved from https://doi.org/10.1371/journal.pone.0156571.
• Shahhosseini, M., Martinez-Feria, R., Hu, G., & Archontoulis, S. (2019). Maize yield and nitrate loss prediction with machine learning algorithms. Environmental Research Letters, 14. https://doi.org/10.1088/1748-9326/ab5268.
• Jiang, D., Yang, X., Clinton, N., & Wang, N. (2004). An artificial neural network model for estimating crop yields using remotely sensed information. International Journal of Remote Sensing, 25(9), 1723–1732. https://doi.org/10.1080/0143116031000150068.
• Khaki, S., Wang, L., & Archontoulis, S. V. (2020). A CNN-RNN Framework for Crop Yield Prediction. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.01750.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539.
• You, J., Li, X., Low, M., Lobell, D., & Ermon, S. (2017). Deep gaussian process for crop yield prediction based on remote sensing data. In Thirty-First AAAI conference on artificial intelligence.
• Khaki, S., & Wang, L. (2019). Crop Yield Prediction Using Deep Neural Networks. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.00621.
• Kim, N., Ha, K.-J., Park, N.-W., Cho, J., Hong, S., & Lee, Y.-W. (2019). A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015. ISPRS International Journal of Geo-Information, 8, 240. https://doi.org/10.3390/ijgi8050240.
• Tian, H., Wang, P., Tansey, K., Zhang, J., Zhang, S., & Li, H. (2021). An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China. Agricultural and Forest Meteorology, 310, 108629. https://doi.org/https://doi.org/10.1016/j.agrformet.2021.108629.
• Jayaraman, A. K., Murugappan, A., Trueman, T. E., & Cambria, E. (2021). Comment toxicity detection via a multichannel convolutional bidirectional gated recurrent unit. Neurocomputing, 441, 272–278. https://doi.org/https://doi.org/10.1016/j.neucom.2021.02.023.
• Wang, J., Zhang, Y., Yu, L.-C., & Zhang, X. (2022). Contextual sentiment embeddings via bi-directional GRU language model. Knowledge-Based Systems, 235, 107663. https://doi.org/https://doi.org/10.1016/j.knosys.2021.107663.
• Hu, L., Wang, C., Ye, Z., & Wang, S. (2021). Estimating gaseous pollutants from bus emissions: A hybrid model based on GRU and XGBoost. Science of The Total Environment, 783, 146870.
• Chen, J. X., Jiang, D. M., & Zhang, Y. N. (2019). A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification. IEEE Access, 7, 118530–118540. https://doi.org/10.1109/ACCESS.2019.2936817.
• Aggarwal, C. C. (2018). Neural Networks and Deep Learning. Neural Networks and Deep Learning. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94463-0.
• Liu, G., & Guo, J. (2019). Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337, 325–338. https://doi.org/https://doi.org/10.1016/j.neucom.2019.01.078.
• Pang, Z., Niu, F., & O’Neill, Z. (2020). Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons. Renewable Energy, 156, 279–289. https://doi.org/https://doi.org/10.1016/j.renene.2020.04.042.
• Wang, J. Q., Du, Y., & Wang, J. (2020). LSTM based long-term energy consumption prediction with periodicity. Energy, 197, 117197.
• Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021). Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors. https://doi.org/10.3390/s21082852.
• Çetiner, H., & Çetiner, İ. (2021). Analysis of Different Regression Algorithms for the Estimate of Energy Consumption. European Journal of Science and Technology, (31), 23–33. https://doi.org/10.31590/ejosat.969539.
• ArunKumar, K. E., Kalaga, D. V, Kumar, C. M. S., Kawaji, M., & Brenza, T. M. (2022). Comparative analysis of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells, Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA) for forecasting COVID-19 trends. Alexandria Engineering Journal.
• Ahmadzadeh, E., Kim, H., Jeong, O., Kim, N., & Moon, I. (2022). A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification. IEEE Access.
• Bhadouria, S. S., & Gupta, S. (2022). Combined LSTM GRU Model for Prediction of Congestion State in QUIC Protocol. In Proceedings of International Conference on Computational Intelligence and Emerging Power System (pp. 123–131). Springer.
• Li, W., Wu, H., Zhu, N., Jiang, Y., Tan, J., & Guo, Y. (2021). Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Information Processing in Agriculture, 8(1), 185–193.