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:: Volume 14, Issue 1 (9-2024) ::
JGST 2024, 14(1): 1-12 Back to browse issues page
Development of an Ensemble Learning Approach for Soybean Yield Prediction using Satellite and Meteorological Data
Ali Sabzali Yameqani * , Ali Asghar Alesheikh , Mostafa Majidi
Abstract:   (999 Views)
Accurate crop yield estimation is important for many agricultural issues, including agricultural management, national food policies, and international crop trade. For this purpose, various methods are used to predict product performance, and the use of satellite images increases every day. Satellite remote sensing techniques that cover large areas continuously can help in more accurate assessment of crop yields. This research develops an optimal model for predicting soybean yield in the Midwest region of the United States. The ensemble learning hybrid model was tested using satellite images and meteorological data during the dominant growth period. In particular, the Golden Eagle Optimization (GEO) algorithm was used to adjust the hyper-parameters of the XGBoost model to provide the best possible configuration to improve accuracy. The results showed that the GEO-XGBoost model had good results for soybean crop (R equal to 0.9377 and RMSE equal to 0.2394 tons/ha). These results show that the optimized GEO-XGBoost model can provide accurate predictions for soybean yield under different weather conditions and can also be extended to predict other crops in the future.
Article number: 1
Keywords: Ensemble Learning, Yield Prediction, Soybean, XGBoost, Golden Eagle Optimization
Full-Text [PDF 818 kb]   (381 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2024/07/7
References
1. N. Kim, K.-J. Ha, N.-W. Park, J. Cho, S. Hong, and Y.-W. Lee, "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, vol. 8, no. 5, p. 240, 2019. [DOI:10.3390/ijgi8050240]
2. N. Kim et al., "An artificial intelligence approach to prediction of corn yields under extreme weather conditions using satellite and meteorological data," Applied Sciences, vol. 10, no. 11, p. 3785, 2020. [DOI:10.3390/app10113785]
3. Y. Ma, Z. Zhang, Y. Kang, and M. Özdoğan, "Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach," Remote Sensing of Environment, vol. 259, p. 112408, 2021. [DOI:10.1016/j.rse.2021.112408]
4. S. Zare Naghadehi, M. Asadi, M. Maleki, S.-M. Tavakkoli-Sabour, J. L. Van Genderen, and S.-S. Saleh, "Prediction of urban area expansion with implementation of MLC, SAM and SVMs' classifiers incorporating artificial neural network using landsat data," ISPRS International Journal of Geo-Information, vol. 10, no. 8, p. 513, 2021. [DOI:10.3390/ijgi10080513]
5. M. Asadi, A. Oshnooei-Nooshabadi, S.-a. Saleh, F. Habibnezhad, S. Sarafraz-Asbagh, and J. L. Van Genderen, "Simulation of Urban Sprawl by Comparison Cellular Automata-Markov and ANN," 2022. [DOI:10.20944/preprints202208.0119.v1]
6. Y. Li et al., "A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering," International Journal of Applied Earth Observation and Geoinformation, vol. 118, p. 103269, 2023. [DOI:10.1016/j.jag.2023.103269]
7. N. Kim and Y.-W. Lee, "Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State: A Case of Iowa State," Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, vol. 34, no. 4, pp. 383-390, 2016. [DOI:10.7848/ksgpc.2016.34.4.383]
8. G. Ghazaryan, S. Skakun, S. König, E. E. Rezaei, S. Siebert, and O. Dubovyk, "Crop yield estimation using multi-source satellite image series and deep learning," in IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 2020: IEEE, pp. 5163-5166. [DOI:10.1109/IGARSS39084.2020.9324027]
9. M. Shahhosseini, G. Hu, and S. V. Archontoulis, "Forecasting corn yield with machine learning ensembles," Frontiers in Plant Science, vol. 11, p. 1120, 2020. [DOI:10.3389/fpls.2020.01120]
10. S. Khaki, L. Wang, and S. V. Archontoulis, "A CNN-RNN framework for crop yield prediction," Frontiers in Plant Science, vol. 10, p. 1750, 2020. [DOI:10.3389/fpls.2019.01750]
11. S. Cho et al., "A comparative evaluation of multiple meteorological datasets for the rice yield prediction at the county level in South Korea," Korean Journal of Remote Sensing, vol. 37, no. 2, pp. 337-357, 2021.
12. S. Jeong, J. Ko, and J.-M. Yeom, "Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea," Science of The Total Environment, vol. 802, p. 149726, 2022. [DOI:10.1016/j.scitotenv.2021.149726]
13. S. Karthick and N. Gomathi, "IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm," Medical & Biological Engineering & Computing, vol. 62, no. 3, pp. 925-940, 2024. [DOI:10.1007/s11517-023-02973-1]
14. S. Boriratrit, P. Fuangfoo, C. Srithapon, and R. Chatthaworn, "Adaptive meta-learning extreme learning machine with golden eagle optimization and logistic map for forecasting the incomplete data of solar irradiance," Energy and AI, vol. 13, p. 100243, 2023. [DOI:10.1016/j.egyai.2023.100243]
15. P. Toscano, A. Castrignanò, S. F. Di Gennaro, A. V. Vonella, D. Ventrella, and A. Matese, "A precision agriculture approach for durum wheat yield assessment using remote sensing data and yield mapping," Agronomy, vol. 9, no. 8, p. 437, 2019. [DOI:10.3390/agronomy9080437]
16. S. A. Khosravani Shariati and A. Abbasi, "Prediction of wheat yield with precipitation data and normalized index of vegetation difference by machine learning algorithms," presented at the In the 13th International Congress of Civil Engineering, 2023.
17. J. Sun, Z. Lai, L. Di, Z. Sun, J. Tao, and Y. Shen, "Multilevel deep learning network for county-level corn yield estimation in the us corn belt," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5048-5060, 2020. [DOI:10.1109/JSTARS.2020.3019046]
18. N.-T. Son et al., "Machine learning approaches for rice crop yield predictions using time-series satellite data in Taiwan," International Journal of Remote Sensing, vol. 41, no. 20, pp. 7868-7888, 2020. [DOI:10.1080/01431161.2020.1766148]
19. G.-H. Kwak, C.-w. Park, K.-d. Lee, S.-i. Na, H.-y. Ahn, and N.-W. Park, "Potential of hybrid CNN-RF model for early crop mapping with limited input data," Remote Sensing, vol. 13, no. 9, p. 1629, 2021. [DOI:10.3390/rs13091629]
20. K. Alibabaei, P. D. Gaspar, and T. M. Lima, "Crop yield estimation using deep learning based on climate big data and irrigation scheduling," Energies, vol. 14, no. 11, p. 3004, 2021. [DOI:10.3390/en14113004]
21. M. Shahhosseini, G. Hu, I. Huber, and S. V. Archontoulis, "Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt," Scientific reports, vol. 11, no. 1, p. 1606, 2021-a. [DOI:10.1038/s41598-020-80820-1]
22. M. Shahhosseini, G. Hu, S. Khaki, and S. V. Archontoulis, "Corn yield prediction with ensemble CNN-DNN," Frontiers in plant science, vol. 12, p. 709008, 2021-b. [DOI:10.3389/fpls.2021.709008]
23. P. Lang et al., "Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province," Frontiers in Plant Science, vol. 13, p. 1048479, 2023. [DOI:10.3389/fpls.2022.1048479]
24. M. Rasoulinia and A. Sharifi, "Potato yield estimation using Sentinel-2 satellite images, Case study: Sarab city," Journal of Geomatics Science and Technology, vol. 11, no. 1, pp. 107-116, 2021.
25. M. Ahangarha, M. Saadat Seresht, R. Shahhoseini, and S. Seyyedi, "Crop land change monitoring based on deep learning algorithm using multi-temporal hyperspectral images," Journal of Geomatics Science and Technology, vol. 10, no. 2, pp. 79-89, 2020.
26. L. O. Colombo-Mendoza, M. A. Paredes-Valverde, M. d. P. Salas-Zárate, and R. Valencia-García, "Internet of Things-driven data mining for smart crop production prediction in the peasant farming domain," Applied Sciences, vol. 12, no. 4, p. 1940, 2022. [DOI:10.3390/app12041940]
27. M. U. Ahmed and I. Hussain, "Prediction of wheat production using machine learning algorithms in northern areas of Pakistan," Telecommunications policy, vol. 46, no. 6, p. 102370, 2022. [DOI:10.1016/j.telpol.2022.102370]
28. L. Schmidt, M. Odening, J. Schlanstein, and M. Ritter, "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, vol. 196, p. 103345, 2022. [DOI:10.1016/j.agsy.2021.103345]
29. T. Van Klompenburg, A. Kassahun, and C. Catal, "Crop yield prediction using machine learning: A systematic literature review," Computers and electronics in agriculture, vol. 177, p. 105709, 2020. [DOI:10.1016/j.compag.2020.105709]
30. K. Meghraoui, I. Sebari, J. Pilz, K. Ait El Kadi, and S. Bensiali, "Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges," Technologies, vol. 12, no. 4, p. 43, 2024. [DOI:10.3390/technologies12040043]
31. A. Oikonomidis, C. Catal, and A. Kassahun, "Hybrid deep learning-based models for crop yield prediction," Applied artificial intelligence, vol. 36, no. 1, p. 2031822, 2022. [DOI:10.1080/08839514.2022.2031823]
32. S. Luo, S. Zhang, and H. Cong, "Research on consumer purchasing prediction based on xgboost algorithm," in 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 2021: IEEE, pp. 1173-1176. [DOI:10.1109/ICAICA52286.2021.9497944]
33. A. Mohammadi-Balani, M. D. Nayeri, A. Azar, and M. Taghizadeh-Yazdi, "Golden eagle optimizer: A nature-inspired metaheuristic algorithm," Computers & Industrial Engineering, vol. 152, p. 107050, 2021. [DOI:10.1016/j.cie.2020.107050]
34. W. Chen, H. Zhang, M. K. Mehlawat, and L. Jia, "Mean-variance portfolio optimization using machine learning-based stock price prediction," Applied Soft Computing, vol. 100, p. 106943, 2021. [DOI:10.1016/j.asoc.2020.106943]
35. D. Xiong, "Crop growth remote sensing monitoring and its application," Sensors & Transducers, vol. 169, no. 4, p. 174, 2014.
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Sabzali Yameqani A, Alesheikh A A, Majidi M. Development of an Ensemble Learning Approach for Soybean Yield Prediction using Satellite and Meteorological Data. JGST 2024; 14 (1) : 1
URL: http://jgst.issgeac.ir/article-1-1193-en.html


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Volume 14, Issue 1 (9-2024) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology