This paper is the first attempt to use machine learning approach on the prediction of field-level annual crop planting from historical crop planting maps.
This paper is the first attempt to use machine learning approach on the prediction of field-level annual crop planting from historical crop planting maps.
Jul 15, 2024 � The proposed framework was first tested at Lancaster County of Nebraska State, then scaled up to the U.S. Corn Belt. According to the experiment�...
Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps. https://doi.org/10.1016/j.compag.2019.104989�...
Zhang et al. [38] demonstrated the use of a deep learning model to forecast the yearly crop planting in the U.S. Corn Belt, with an R 2 value of 0.9, using�...
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Jan 15, 2021 � The machine learning models are developed using a data set spanning from 1984 to 2018 to predict corn yield in three US Corn Belt states (�...
Machine-learned prediction of annual crop planting in the US Corn Belt based on historical crop planting maps. Computers and Electronics in Agriculture, 166�...
Oct 24, 2022 � The proposed machine learning model can be used to predict the crop cover map from the historical CDL time se- ries. Our study has shown that�...
They further implemented a crop sequence-based machine learning framework for prediction of crop cover maps (Zhang et al., 2019b). In this paper, we present a�...
Mar 20, 2023 � Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps. Comput. Electron. Agric�...