Hybrid Deep Learning Implementation for Crop Yield Prediction

Yıl 2023, Cilt: 23 Sayı: 3, 648 - 660, 28.06.2023

Halit Çetiner

https://doi.org/10.35414/akufemubid.1116187

Öz

Agriculture producers should be supported technologically in order to continue production in a way that meets the worldwide food supply and demand. Automatic realization of crop yield estimation calculation is a desired need of farmers. Automatic yield estimation also facilitates the work of agricultural producers with different goals such as imports and exports. To achieve the stated objectives, deep learning models have been developed that estimated yield using parameters such as the amount of water per hectare, the average amount of sunlight received by the hectare, the amount of fertilization per hectare, the number of pesticides used per hectare, and the area of cultivation. With the hybrid model created by combining the strengths of the LSTM and CNN models developed within the scope of this article, the success rate of data prediction has increased with fine adjustments. Success rates of 89.71 R2, 0.0035 MSE, 0.0248 RMSE, 0.0461 MAE, and 10.10 MAPE have been achieved with the Proposed hybrid model. This model is competitive with similar studies with the stated values.

Anahtar Kelimeler

Crop, Yield estimation, CNN, LSTM, Hybrid model

Hybrid Deep Learning Implementation for Crop Yield Prediction

Öz

Tarım üreticilerinin dünya çapındaki gıda arz ve talebini karşılayacak şekilde üretime devam edebilmesi için teknolojik olarak desteklenmesi gerekmektedir. Mahsul verim tahmini hesaplamasının otomatik olarak gerçekleştirilmesi, çiftçilerin arzu ettiği bir ihtiyaçtır. Otomatik olarak verim tahmini gerçekleştirilmesi ithalat ve ihracat gibi farklı hedefleri olan tarım üreticisinin işlerini de kolaylaştırmaktadır. Belirtilen amaçlara ulaşabilmek için hektar başına su miktarı, hektar tarafından alınan ortalama güneş ışığı miktarı, hektar başına verilen gübreleme miktarı, hektar başına kullanılan pestisit miktarı, ekim yapılan alan bölgesi parametrelerini kullanarak verim tahmini gerçekleştiren derin öğrenme modelleri geliştirilmiştir. Bu makale kapsamında geliştirilen LSTM ve CNN modellerinin güçlü yanları birleştirilerek oluşturulan hibrit modelde ile veri tahmin başarı oranının ince ayarlamalar ile artırılmıştır. Önerilen hibrit model ile 89.71 R2, 0.0035 MSE, 0.0248 RMSE, 0.0461 MAE, ve 10.10 MAPE başarı oranlarına ulaşılmıştır. Bu model, belirtilen değerlerle benzer çalışmalarla rekabet edebilir seviyededir.

Anahtar Kelimeler

Mahsul, Verim tahmini, CNN, LSTM, Hibrit model

Kaynakça

• 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
• 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
• 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
• 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.
• 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
• 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.
• 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
• 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
• 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
• Çetiner, H., & Kara, B. 2022. Recurrent Neural Network Based Model Development for Wheat Yield Forecasting. Journal of Engineering Sciences of Adiyaman University, 9(16), 204–218. https://doi.org/10.54365/adyumbd.1075265
• 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
• Cline, W. R. 2007. Global warming and agriculture: End-of-century estimates by country. Peterson Institute.
• Deutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B., & Naylor, R. L. 2018. Increase in crop losses to insect pests in a warming climate. Science, 361(6405), 916 LP – 919. https://doi.org/10.1126/science.aat3466
• Dodds, F., & Bartram, J. 2016. The water, food, energy and climate Nexus: Challenges and an agenda for action. Routledge.
• 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.
• 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. 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
• 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/10.1016/j.neucom.2021.02.023
• 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
• Lago, J., De Brabandere, K., De Ridder, F., & De Schutter, B. 2018. Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data. Solar Energy, 173, 566–577. https://doi.org/https://doi.org/10.1016/j.solener.2018.07.050
• LeCun, Y., Bengio, Y., & Hinton, G. 2015. Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
• Liu, G., & Guo, J. 2019. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337, 325–338. https://doi.org/10.1016/j.neucom.2019.01.078
• 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
• Qing, X., & Niu, Y. 2018. Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy, 148, 461–468. https://doi.org/https://doi.org/10.1016/j.energy.2018.01.177
• 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
• Srivastava, S., & Lessmann, S. 2018. A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Solar Energy, 162, 232–247. https://doi.org/https://doi.org/10.1016/j.solener.2018.01.005
• 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
• 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
• Wang, J. Q., Du, Y., & Wang, J. 2020. LSTM based long-term energy consumption prediction with periodicity. Energy, 197, 117197.
• Ye, L., Cao, Z., & Xiao, Y. 2017. DeepCloud: Ground-based cloud image categorization using deep convolutional features. IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5729–5740.
• 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.