Corpus ID: 44083290

Predicting County Level Corn Yields Using Deep Long Short Term Memory Models

@article{Jiang2018PredictingCL,
  title={Predicting County Level Corn Yields Using Deep Long Short Term Memory Models},
  author={Zehui Jiang and Chao Liu and Nathan P. Hendricks and Baskar Ganapathysubramanian and Dermot J. Hayes and Soumik Sarkar},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.12044}
}
Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. This paper is the first to employ Long Short-Term Memory (LSTM), a special form of Recurrent Neural Network (RNN) method to predict corn yields. A cross sectional time series of county-level corn yield and… Expand
Deep Time Series Attention Models for Crop Yield Prediction and Insights
Soybean yield depends on both environmental and genetic factors which can be difficult to dissect as multiple replications of many genotypes are needed in diverse environmental conditions. We aim toExpand
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model
TLDR
The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or L STM model in both end-of-season and in-season soybean yield prediction in CONUS at the county-level. Expand
Crop yield prediction integrating genotype and weather variables using deep learning
TLDR
A Long Short Term Memory—Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments and developed a temporal attention mechanism for LSTM models. Expand
Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt
TLDR
This work proposed a novel multilevel deep learning model coupling RNN and CNN to extract both spatial and temporal features and used it to predict corn yield from 2013 to 2016 at the county-level. Expand
Estimating the Impact of Weather on CBOT Corn Futures Prices
We apply machine learning methods to weather and soil data to evaluate their impact on Chicago Board of Trade (CBOT) corn futures prices. We try to find out which weather information: historical,Expand
Prediction of Strawberry Yield and Farm Price Utilizing Deep Learning
TLDR
After utilizing an aggregated performance measure to find the best model, the Attention-CNN-LSTM model proved to be the best compared to the rest of the deployed conventional ML models as well as the compound and simple DL models. Expand
Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches
TLDR
A two-branch deep learning model is established to predict winter wheat yield in the main producing regions of China at the county level and showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction. Expand
DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting
TLDR
This work proposes DeepYield, a combined structure, that integrates the ConvLSTM layers with the 3-Dimensional CNN (3DCNN) for more accurate and reliable spatiotemporal feature extraction in crop yield forecasting. Expand
Imputation Impact on Strawberry Yield and Farm Price Prediction Using Deep Learning
TLDR
The overall AAGM of the compound DL and ensemble prediction models across all the 1, 2, 3, and 4 weeks ahead price predictions confirm that using LIME highly improves the prediction performance of the ensemble and its compound DL components. Expand
Deep Learning Ensemble Based Model for Time Series Forecasting Across Multiple Applications
TLDR
The experiment results show that across the various examined applications, the proposed model which is a stacking ensemble of the AC-LSTM and ACV-L STM using a linear SVR is the best performing based on the aggregated measure. Expand
...
1
2
3
...

References

SHOWING 1-10 OF 25 REFERENCES
A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
TLDR
This work proposes a data-driven approach that is "gray box" i.e. that seamlessly utilizes expert knowledge in constructing a statistical network model for corn yield forecasting, developed on Bayesian network analysis to build a Directed Acyclic Graph between predictors and yield. Expand
Recurrent Neural Networks for Multivariate Time Series with Missing Values
TLDR
Novel deep learning models are developed based on Gated Recurrent Unit, a state-of-the-art recurrent neural network that takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Expand
Artificial neural networks for corn and soybean yield prediction
The Maryland Water Quality Improvement Act of 1998 requires mandatory nutrient management planning on all agricultural land in Maryland. Nutrient management specialists need simple and accurateExpand
Three essays on weather and crop yield
The general theme of this dissertation is the study of impacts of weather variability on crop yields, with each chapter addressing a specific topic related to this theme. Chapter 2 tests theExpand
Long Short-Term Memory
TLDR
A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Expand
Recurrent neural networks for time series classification
TLDR
The dynamic behaviour of the RNN is used to categorize input sequences into different specified classes and enables the user to assess efficiently the degree of reliability of the classification result. Expand
Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
TLDR
Yields increase with temperature but that temperatures above these thresholds are very harmful, suggesting limited historical adaptation of seed varieties or management practices to warmer temperatures because the cross-section includes farmers' adaptations to warmer climates and the time-series does not. Expand
Are Yearly Variations in Crop Yield Really Random
The study reported here is part of a research project, under the Research and Marketing Act of 1946, entitled Anticipating Year-to-Year Changes in Market Supplies Due to Changes in Yields Per Acre.Expand
Development of a neural network for soybean rust epidemics
The objective of this study was to develop a neural network to predict soybean rust disease severity for a single soybean cultivar. Data available for development consisted of sequential weeklyExpand
Spatial validation of crop models for precision agriculture
Abstract Spatial measurements of yield using technological advances like on-the-go yield monitoring systems have clearly shown large within-field variability in crop yields suggesting that fieldExpand
...
1
2
3
...