A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

2019 ◽  
Vol 26 (3) ◽  
pp. 1754-1766 ◽  
Author(s):  
Hao Jiang ◽  
Hao Hu ◽  
Renhai Zhong ◽  
Jinfan Xu ◽  
Jialu Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Geospatial Data ◽  
Corn Belt ◽  
Corn Yield ◽  
Learning Approach ◽  
County Level ◽  
Yield Estimation ◽  
The Us

scholarly journals DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

2020 ◽  
Vol 15 (3) ◽  
pp. 034016
Author(s):  
Tao Lin ◽  
Renhai Zhong ◽  
Yudi Wang ◽  
Jinfan Xu ◽  
Hao Jiang ◽  
...  
Keyword(s):  
Corn Yield ◽  
County Level ◽  
Yield Estimation ◽  
Learning Framework ◽  
Temporal Learning

A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin

2020 ◽  
Vol 13 (3) ◽  
pp. 915-927 ◽  
Author(s):  
Dostdar Hussain ◽  
Tahir Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
River Basin ◽  
Time Series Prediction ◽  
Learning Approach

scholarly journals A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

2019 ◽  
Vol 38 ◽  
pp. 233-240 ◽  
Author(s):  
Mattia Carletti ◽  
Chiara Masiero ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach

scholarly journals Corn Yield Prediction With Ensemble CNN-DNN

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohsen Shahhosseini ◽  
Guiping Hu ◽  
Saeed Khaki ◽  
Sotirios V. Archontoulis
Keyword(s):  
Root Mean Square ◽  
Predictive Performance ◽  
Ensemble Method ◽  
Corn Belt ◽  
Corn Yield ◽  
Yield Prediction ◽  
Mean Square ◽  
Data Set ◽  
The Us ◽  
Spatio Temporal

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 developed data set is a combination of management, environment, and historical corn yields from 1980 to 2019. Two scenarios for ensemble creation are considered: homogenous and heterogenous ensembles. In homogenous ensembles, the base CNN-DNN models are all the same, but they are generated with a bagging procedure to ensure they exhibit a certain level of diversity. Heterogenous ensembles are created from different base CNN-DNN models which share the same architecture but have different hyperparameters. Three types of ensemble creation methods were used to create several ensembles for either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble Method (GEM), and stacked generalized ensembles. Results indicated that both designed ensemble types (heterogenous and homogenous) outperform the ensembles created from five individual ML models (linear regression, LASSO, random forest, XGBoost, and LightGBM). Furthermore, by introducing improvements over the heterogenous ensembles, the homogenous ensembles provide the most accurate yield predictions across US Corn Belt states. This model could make 2019 yield predictions with a root mean square error of 866 kg/ha, equivalent to 8.5% relative root mean square and could successfully explain about 77% of the spatio-temporal variation in the corn grain yields. The significant predictive power of this model can be leveraged for designing a reliable tool for corn yield prediction which will in turn assist agronomic decision makers.

scholarly journals Trends and Variability in U.S. Corn Yields Over the Twentieth Century

2005 ◽  
Vol 9 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Christopher J. Kucharik ◽  
Navin Ramankutty
Keyword(s):  
Twentieth Century ◽  
Growth Rates ◽  
The United States ◽  
Yield Variability ◽  
Corn Belt ◽  
Corn Yield ◽  
County Level ◽  
Agricultural Revolution ◽  
Decadal Scale ◽  
The U.S

Abstract The United States is currently responsible for 40%–45% of the world’s corn supply and 70% of total global exports [the U.S. Department of Agriculture–National Agricultural Statistics Service (USDA–NASS)]. Therefore, analyses of the spatial and temporal patterns of historical U.S. corn yields might provide insight into future crop-production potential and food security. In this study, county-level maize yield data from 1910 to 2001 were used to characterize the spatial heterogeneity of yield growth rates and interannual yield variability across the U.S. Corn Belt. Widespread decadal-scale changes in corn yield variability and yield growth rates have occurred since the 1930s across the Corn Belt, but the response has varied substantially with geographic location. Northern portions of the Great Plains have experienced consistently high interannual corn yield variability, averaging 30%–40% relative to the mean. Increasing usage of irrigation in Nebraska, Kansas, and Texas, since the 1950s, has helped boost yields by 75%–90% over rain-fed corn, creating a yield gap of 2–4 T ha−1 between irrigated and nonirrigated corn that could potentially be exploited in other regions. Furthermore, irrigation has reduced interannual variability by a factor of 3 in these same regions. A small region from eastern Iowa into northern Illinois and southern Wisconsin has experienced minimal interannual yield variability, averaging only 6%–10% relative to mean yields. This paper shows that the choice of time period used for statistical analysis impacted conclusions drawn about twentieth-century trends in corn yield variability. Widespread increases in yield variability were apparent from 1950 onward, but were not significant over the entire 1930–2001 period. There is also evidence that yield variability decreased from the early 1990s to 2001. Corn yield growth rates peaked at an annual-average rate of 3%–5% in the 1960s (124.5 kg ha−1 yr−1), but have steadily declined to a relative rate of 0.78% yr−1 (49.2 kg ha−1 yr−1) during the 1990s. A general inverse relationship between increasing corn yield and decreasing yield growth rates was noted after county-level yields reached 4 T ha−1, suggesting that widespread, significant increases in corn yield are not likely to take place in the future, particularly on irrigated land, without a second agricultural revolution.

Crop disease classification using deep learning approach: an overview and a case study

2020 ◽  
pp. 173-195
Author(s):  
Krishnaswamy Rangarajan Aravind ◽  
Prabhakar Maheswari ◽  
Purushothaman Raja ◽  
Cezary Szczepański
Keyword(s):  
Deep Learning ◽  
Disease Classification ◽  
Learning Approach ◽  
Crop Disease

Mid‐season county‐level corn yield forecast for US Corn Belt integrating satellite imagery and weather variables

Crop Science ◽  
2020 ◽  
Vol 60 (2) ◽  
pp. 739-750 ◽  
Author(s):  
Rai Schwalbert ◽  
Telmo Amado ◽  
Luciana Nieto ◽  
Geomar Corassa ◽  
Charles Rice ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Corn Belt ◽  
Corn Yield ◽  
County Level ◽  
Weather Variables ◽  
Yield Forecast
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