Water and Irrigation Management

Water and Irrigation Management

Barley Yield Forecasting Based on Remote Sensing Data and Xgboost and SVM Machine Learning Algorithms

Document Type : Research Paper

Authors
Hanie Bourbour 1
Mohammad Abdolahipour 1
Hojjat Abdollahi 2
Mahmoud Mashal 1
1 Department of Water Engineering, Faculty of Agricultural Technology, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran.
2 Department of Electrical and Computer Engineering, University of Denver, Denver, Colorado, US.
10.22059/jwim.2023.360327.1083
Abstract
In recent years, the use of machine learning algorithms has been a promising way to improve crop yield predictions, especially when using non-linear relationships. This research was conducted with the aim of evaluating the yield estimation of irrigated and rainfed barley as well as the total yield of barley produced in the provincial centers of Iran using remote sensing data and machine learning methods including XGBoost and SVM. The results showed that by using climatic data, drought indices and plant indices of remote sensing and also XGBoost and SVM algorithms, it is possible to reliably estimate barley yield in different regions of the country with different climates. In general, the RMSE error obtained for both models was acceptable (0.41 and 0.77 t/ha). The R2 determination coefficient values for XGBoost and SVR algorithms in modeling rainfed barley cultivation performance were equal to 0.2 and 0.22 respectively, in irrigated barley performance were equal to 0.52 and 0.55 and for total barley were equal to 0.66 and 0.65, indicating that the rainfed yield modeling were not as suitable as irrigated and total barely yield modeling. The RBF kernel was chosen as the best kernel to use for the SVM algorithm. Also, in this research, while examining the effects of change in train and test data dividing, the parameters of precipitation, temperature, and evapotranspiration were determined as the most important parameters affecting the performance of the barley yield for both algorithms in different evaluated conditions.
Keywords
Barely production
Machine learning algorithms
Remote sensing
Yield prediction
Subjects
  1. Alasti, O., Zeinali, E., Soltani, A., & Torabi, B. (2020). Estimation of yield gap and the potential of rainfed barley production increase in Iran. Journal of Crop Production, 13(3), 41-60.
  2. Alvarez, R. (2009). Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. European Journal of Agronomy, 30(2), 70-77.
  3. Balaghi, R., Tychon, B., Eerens, H., & Jlibene, M. (2008). Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation, 10(4), 438-452.
  4. Bocca, F. F., & Rodrigues, L. H. A. (2016). The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling. Computers and Electronics in Agriculture, 128, 67-76.
  5. Bouras, E. H., Jarlan, L., Er-Raki, S., Albergel, C., Richard, B., Balaghi, R., & Khabba, S. (2020). Linkages between rainfed cereal production and agricultural drought through remote sensing indices and a land data assimilation system: A case study in Morocco. Remote Sensing, 12(24), 4018.
  6. Bouras, E. H., Jarlan, L., Er-Raki, S., Balaghi, R., Amazirh, A., Richard, B., & Khabba, S. (2021). Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in Morocco. Remote Sensing, 13(16), 3101.
  7. Cai, X., & Sharma, B. R. (2010). Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the Indo-Gangetic river basin. Agricultural Water Management, 97(2), 309-316.
  8. Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Han, J., & Li, Z. (2020). Identifying the contributions of multi-source data for winter wheat yield prediction in China. Remote Sensing, 12(5), 750.
  9. Cao, J., Wu, E., Wu, S., Fan, R., Xu, L., Ning, K., Li, Y., Lu, R., Xu, X., & Zhang, J. (2022). Spatiotemporal dynamics of ecological condition in Qinghai-Tibet Plateau based on Remotely Sensed Ecological Index. Remote Sensing, 14(17), 4234.
  10. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 785.
  11. Chen, Z., Wang, W., & Fu, J. (2020). Vegetation response to precipitation anomalies under different climatic and biogeographical conditions in China. Scientific Reports, 10(1), 1-16.
  12. Du, L., Tian, Q., Yu, T., Meng, Q., Jancso, T., Udvardy, P., & Huang, Y. (2013). A comprehensive drought monitoring method integrating MODIS and TRMM data. International Journal of Applied Earth Observation and Geoinformation, 23, 245-253.
  13. Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Lu, X., & Xiang, Y. (2018). Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion and Management, 164, 102-111.
  14. Filippi, P., Jones, E. J., Wimalathunge, N. S., Somarathna, P. D., Pozza, L. E., Ugbaje, S. U., Jephcott, T. G., Paterson, S. E., Whelan, B. M., & Bishop, T. F. (2019). An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precision Agriculture, 20, 1015-1029.
  15. 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.
  16. Guo, W. W., & Xue, H. (2012). An incorporative statistic and neural approach for crop yield modelling and forecasting. Neural Computing and Applications, 21, 109-117.
  17. Han, J., Zhang, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., & Zhang, J. (2020). Prediction of winter wheat yield based on multi-source data and machine learning in China. Remote Sensing, 12(2), 236.
  18. Huang, X., Liu, J., Zhu, W., Atzberger, C. & Liu, Q. (2019). The optimal threshold and vegetation index time series for retrieving crop phenology based on a modified dynamic threshold method. Remote Sensing, 11(23), 2725.
  19. Jarray, N., Abbes, A. B., & Farah, I. R. (2022). A Machine learning framework for cereal yield forecasting using heterogeneous data. In International Conference on Intelligent Systems Design and Applications. Cham: Springer Nature Switzerland. 21-30.
  20. 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.
  21. Johnson, M. D., Hsieh, W. W., Cannon, A. J., Davidson, A., & Bédard, F. (2016). Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agricultural and Forest Meteorology, 218, 74-84.
  22. Kang, Y., Ozdogan, M., Zhu, X., Ye, Z., Hain, C., & Anderson, M. (2020). Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. Environmental Research Letters, 15(6), 064005.
  23. Kim, N., & Lee, Y.-W. (2016). Machine learning approaches to corn yield estimation using satellite images and climate data: a case of Iowa State. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 34(4), 383-390.
  24. Li, A., Liang, S., Wang, A., & Qin, J. (2007). Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogrammetric Engineering and Remote Sensing, 73(10), 1149-1157.
  25. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
  26. Liu, W., & Kogan, F. (2002). Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices. International Journal of Remote Sensing, 23(6), 1161-1179.
  27. MacDonald, R. B., & Hall, F. G. (1980). Global crop forecasting. Science, 208(4445), 670-679.
  28. Majnooni Heris, A., Sadraddini, A. A., Nazemi, A. H., & Delir Hassannia, R. (2017). Investigation of advection occurrence in the spring and fall crops growing season in Karkaj region, Tabriz. Water and Soil Science, 27(4), 65-77.
  29. Palanivel, K., & Surianarayanan, C. (2019). An approach for prediction of crop yield using machine learning and big data techniques. International Journal of Computer Engineering and Technology, 10(3), 110-118.
  30. Parviz, L. (2018). Assessing accuracy of barley yield forecasting with integration of climate variables and support vector regression. Annales Universitatis Mariae Curie-Sklodowska, sectio C–Biologia, 73, 1.
  31. Prasad, A. K., Chai, L., Singh, R. P., & Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1), 26-33.
  32. Rahman, M. M., & Robson, A. (2020). Integrating landsat-8 and sentinel-2 time series data for yield prediction of sugarcane crops at the block level. Remote Sensing, 12(8), 1313.
  33. Rasouli, A., & Erfanian, M. (2014). Comparative evaluation of TRMM estimated rainfall and recorded rainfall of ground stations in Lake Urmia basin. Geographical Space Quarterly, 54, 195-217. (In Persian).
  34. Rezapour, S., Jooyandeh, E., Ramezanzade, M., Mostafaeipour, A., Jahangiri, M., Issakhov, A., Chowdhury, S., & Techato, K. (2021). Forecasting rainfed agricultural production in arid and semi-arid lands using learning machine methods: A case study. Sustainability, 13(9), 4607.
  35. Salazar, L., Kogan, F., & Roytman, L. (2007). Use of remote sensing data for estimation of winter wheat yield in the United States. International Journal of Remote Sensing, 28(17), 3795-3811.
  36. Sharifi, A. (2021). Yield prediction with machine learning algorithms and satellite images. Journal of the Science of Food and Agriculture, 101(3), 891-896.
  37. Shojaeeian, A., Mokhtari Chelche, S., Keshtkar, L., & Soleymani Rad, E. (2015). Comparing the performance of parametric and nonparametric methods in land cover classification using Landsat-8 Satellite images (Case study: A part of Dezful city). Scientific-Research Quarterly of Geographical Data, 24(93), 53-64. (In Persian).
  38. Sobrino, J., Coll, C., & Caselles, V. (1991). Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote sensing of environment, 38(1), 19-34.
  39. Tabari, H., Talaee, P. H., Nadoushani, S. M., Willems, P., & Marchetto, A. (2014). A survey of temperature and precipitation based aridity indices in Iran. Quaternary International, 345, 158-166.
  40. Thenkabail, P. S., & Gamage, M. S. D. N. (2004). The use of remote sensing data for drought assessment and monitoring in Southwest Asia (Vol. 85). IWMI.
  41. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  42. Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709.
  43. Zahirnia, A. R., & Matinfar, H. R. (2016). Evaluate the yield of irrigated wheat fields on the basis of data obtained from Landsat 8 in the southwestern province of Khuzestan. In First National Conference on Remote Sensing and GIS in the earth sciences, Shiraz University. (In Persian).
  44. Zeinvand Lorestani, E., Kamkar, B., & Razavi, S. E. (2016). Study on the effect of agricultural management factors on fungal diseases diversity indices and wheat yield in Gorgan using decision tree analysis CART. Cereal Research, 6(4), 489-505. (In Persian).
Water and Irrigation Management
Volume 13, Issue 4
Winter 2024
Pages 1119-1137