activity
20192021
most citedA CNN-RNN Framework for Crop Yield Prediction

612 citations · 612 across the 2 of their papers we have counts for

collaborators

5 papers

q-bio.QM2021

Corn Yield Prediction with Ensemble CNN-DNN

Mohsen Shahhosseini, Guiping Hu, Saeed Khaki +1

We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The deve…

q-bio.QM2020

Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt

Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis +1

This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a h…

stat.AP2020

Forecasting Corn Yield with Machine Learning Ensembles

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. Recent researc…

cs.LG2019612 cited

A CNN-RNN Framework for Crop Yield Prediction

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.…

q-bio.OT2019

Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms

Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu +1

Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scen…