no code implementations • 17 Jul 2022 • Faezeh Akhavizadegan, Javad Ansarifar, Lizhi Wang, Sotirios V. Archontoulis
Optimizing management practices and selecting the best cultivar for planting play a significant role in increasing agricultural food production and decreasing environmental footprint.
no code implementations • 29 May 2021 • Mohsen Shahhosseini, Guiping Hu, Saeed Khaki, Sotirios V. Archontoulis
Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles.
no code implementations • 28 Jul 2020 • Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis, Isaiah Huber
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt.
no code implementations • 18 Jan 2020 • Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis
The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields.
4 code implementations • 20 Nov 2019 • Saeed Khaki, Lizhi Wang, Sotirios V. Archontoulis
Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions.
no code implementations • 14 Aug 2019 • Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios V. Archontoulis
We asked: 1) How well do ML meta-models predict maize yield and N losses using pre-season information?