Spectral features fusion of effective criteria on wheat yield prediction

Document Type : Original research


Department of Geomatics Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran


The yield of the wheat crop is affected by the climate and soil parameters such as moisture and nutrients, plant pests and diseases. The main objective of this research is the feature level fusion of multiple effective criteria on the wheat yields using linear and machine learning regression models. The effects of vegetation condition, moisture, nutrients and pests on wheat yield are represented by spectral indices those are extracted from remotely sensed data. Optimum spectral indices are selected as the input features to each of the multiple linear and machine learning regression models such as decision tree, support vector regression and generalized regression neural network. The evaluation of the experimental results in eight wheat fields indicates that the wheat yield prediction based on spectral features fusion show the mean improvement of 0.81 in RMSE comparing with considering only one vegetation index in all regression models. 
Moreover, all investigated machine learning regression models have about 0.03 more performance than the multiple linear regression model as indicated by R2 coefficient. The generalized regression neural network model with the least RMSE error 0.0063 has the best results compared with other machine learning regression models and MLR.


Main Subjects

Atzberger, C. (2013). Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Journal of Remote Sensing, 5(2), 949-981.
Chen, P. (2015). A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing. Journal of Remote Sensing, 7(4), 4527-4548.
Cosh, M. H., White, W. A., Colliander, A., Jackson, T. J., Prueger, J. H., Hornbuckle, B. K., Hunt, E. R., McNairn, H., Powers, J., Walker, V. A., & Bullock, P. (2019). Estimating vegetation water content during the soil moisture active passive validation experiment 2016. Journal of Applied Remote Sensing, 13(1), 014516.
Gonzalez-Sanchez, A., Frausto-Solis, J., & Ojeda-Bustamante, W. (2014). Attribute selection impact on linear and nonlinear regression models for crop yield prediction. The Scientific World Journal, 2014.
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, 2020(12), 236.
Huang, W.,  Yang, Q., Pu, R., & Yang, Sh. (2014). Estimation of nitrogen vertical distribution by bi-directional canopy reflectance in winter wheat. Sensors, 14(11), 20347-20359.
Huang, Y., Chen, Z., Yu, T., Huang, X., & Gu, X. (2018). Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17(9), 1915–1931.
Kastens, J. H., Kastens, T. L., Kastens, D. L. A., Priced, K. P., Martinko, E. A., & Lee, R. Y. (2005). Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sensing of Environment, 99(3), 341 – 356.
Lakhankar, T.,  Krakauer, N., & Khanbilvardi, R. (2009). Applications of microwave remote sensing of soil moisture for agricultural applications. International Journal of Terraspace Science and Engineering, 2(1), 81-91.
Li, F., Miao, Y., Hennig, S. D., Gnyp, M. L., Chen, X., Jia, L., & Bareth, G. (2010). Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture Journal, 11, 335–357.
Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., & Oerke, E. C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment Journal, 128, 21-30.
Miranda, R.,  Nóbrega, R. L. B.,  de Moura, M. S. B., Raghavan, S., & Galvíncio, J. D. (2020). Realistic and simplified models of plant and leaf area indices for a seasonally dry tropical forest. International Journal of Applied Earth Observation & Geoinformation, 85, 101992.
Nagy, A., Szabó, A., Adeniyi, O. D., & Tamás, J. (2021). Wheat yield forecasting for the Tisza River catchment using landsat 8 NDVI and SAVI time series and reported crop statistics. Agronomy, 11(4), 652.
Oliveira, L. F. R., Oliveira, M. L. R., Gomes, F. S., & Santana, R. C. (2017). Estimating foliar nitrogen in eucalyptus using vegetation indexes. Scientia Agricola Journal, 74(2), 142-147.
Palanisamy, Sh., Selvaraj, R., Ramesh, T., & Ponnusamy, J. (2019). Applications of remote sensing in agriculture - A Review. International Journal of Current Microbiology & Applied Sciences, 8(01), 2270-2283.
Pena, J., Tan, Y., & Boonpook, W. (2019). Semantic segmentation based remote sensing data fusion on crops detection. Journal of Computer & Communications, 7(7), 53-64.
Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647–664.
Roell, Y. E., Beucher, A., Møller, P. G., Greve, M. B., & Greve, M. H. (2020). Comparing a Random Forest based prediction of winter wheat yield to historical yield potential. Agronomy, 10(3), 395.
Sellam, V., & Poovammal, E. (2016). Prediction of crop yield using regression analysis. Indian Journal of Science & Technology, 9(38), 1-5.
Shanmugam, L., Adline, A. L. A., Aishwarya, N., & Krithika, G. (2017). Disease detection in crops using remote sensing images. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 112-115.
Sharifi, A. (2021). Yield prediction with machine learning algorithms and satellite images. Journal of the Science of Food & Agriculture, 101(3), 891-896.
Vannoppen, A., Gobin, A., Kotova, L., Top, S., De Cruz, L., Vīksna, A., ... & Termonia, P. (2020). Wheat yield estimation from NDVI and regional climate models in Latvia. Remote Sensing, 12(14), 2206.
Zhang, N.,  Hong, Y., Qin, Q., & Zhu, L. (2013). Evaluation of the visible and shortwave infrared drought index in China. International Journal of Disaster Risk Science, 4(2), 68–76.
Zhao, H., Yang, C., Guo, W., Zhang, L., & Zhang, D. (2020). Automatic estimation of crop disease severity levels based on vegetation index normalization. Remote Sensing, 12(12), 1930.