Satellite Imagery and Deep Learning Combined for Wheat Yield Forecasting
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-68675-7_29
Reference12 articles.
1. Mishra, V., Cruise, J.F., Mecikalski, J.R.: Assimilation of coupled microwave/thermal infrared soil moisture profiles into a crop model for robust maize yield estimates over Southeast United States. Eur. J. Agronomy 123, 126208 (2021). https://doi.org/10.1016/j.eja.2020.126208
2. Amankulova, K., Farmonov, N., Mucsi, L.: Time-series analysis of Sentinel-2 satellite images for sunflower yield estimation. Smart Agric. Technol. 3, 100098 (2023)
3. Jiang, H., et al.: A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: a case study of the US Corn Belt at the county level. Global Change Biol. 26(3), 1754–1766 (2019). https://doi.org/10.1111/gcb.14885
4. Kogan, F., et al.: Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models. Int. J. Appl. Earth Obs. Geoinf. 23, 192–203 (2013). https://doi.org/10.1016/j.jag.2013.01.002
5. Sishodia, R.P., Ray, R.L., Singh, S.K.: Applications of remote sensing in precision agriculture: a review. Remote Sens. 12, 3136 (2020). https://doi.org/10.3390/rs12193136
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