Digital Yield Predictions

Author:

Lipping Tarmo,Ranta Petteri

Publisher

Springer International Publishing

Reference46 articles.

1. Alebele Y, Wang W, Yu W, Zhang X, Yao X, Tian Y, Zhu Y, Cao W, Cheng T (2021) Estimation of crop yield from combined optical and SAR imagery using Gaussian Kernel regression. IEEE J Sel Top Appl Earth Obs Remote Sens 14:10520–10534. https://doi.org/10.1109/JSTARS.2021.3118707. Conference Name: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

2. Bazrafshan O, Ehteram M, Dashti Latif S, Feng Huang Y, Yenn Teo F, Najah Ahmed A, El-Shafie A (2022) Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models. Ain Shams Eng J 13(5):101724. https://doi.org/10.1016/j.asej.2022.101724

3. Bi L, Hu G, Wally O, Tenuta AU, Kandel YR, Mueller DS (2022) A transformer-based approach for early prediction of soybean yield using time-series images. In: Deep learning approaches for yield prediction and crop disease recognition, Doctoral dissertation, vol. 2022. Iowa State University. https://dr.lib.iastate.edu/server/api/core/bitstreams/5464feed-3fd3-4a17-abb3-7221aa41c228/content

4. Chipanshi A, Zhang Y, Kouadio L, Newlands N, Davidson A, Hill H, Warren R, Qian B, Daneshfar B, Bedard F, Reichert G (2015) Evaluation of the integrated Canadian crop yield forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agric For Meteorol 206:137–150. https://doi.org/10.1016/j.agrformet.2015.03.007

5. de Wit A, Boogaard H, Fumagalli D, Janssen S, Knapen R, van Kraalingen D, Supit I, van der Wijngaart R, van Diepen K (2019) 25 years of the WOFOST cropping systems model. Agric Syst 168:154–167. https://doi.org/10.1016/j.agsy.2018.06.018

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