DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting

[1]  E. S. Krayenhoff,et al.  Modeling the angular effect of MODIS LST in urban areas: A case study of Toulouse, France , 2021, Remote Sensing of Environment.

[2]  Fei Liu,et al.  Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems , 2021, Sci. China Inf. Sci..

[3]  Sujay V. Kumar,et al.  High-Resolution SMAP Satellite Soil Moisture Product: Exploring the Opportunities , 2021, Bulletin of the American Meteorological Society.

[4]  Kumaraswamy Ponnambalam,et al.  Ensemble-based machine learning approach for improved leak detection in water mains , 2021, Journal of Hydroinformatics.

[5]  Xiaodi Li,et al.  Input-to-state stability of impulsive reaction–diffusion neural networks with infinite distributed delays , 2021, Nonlinear Dynamics.

[6]  Ping Zhang,et al.  Multi-user security of the tweakable Even-Mansour cipher , 2020, Science China Information Sciences.

[7]  Zhengqiang Zhang,et al.  Event-based fuzzy control for T-S fuzzy networked systems with various data missing , 2020, Neurocomputing.

[8]  H. Moradkhani,et al.  Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting , 2020 .

[9]  C. Hain,et al.  Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring , 2020, Journal of Hydrometeorology.

[10]  Baoyong Zhang,et al.  State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises , 2020, International Journal of Robust and Nonlinear Control.

[11]  Ha Young Kim,et al.  Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation , 2020, Expert Syst. Appl..

[12]  S. Khaki,et al.  A CNN-RNN Framework for Crop Yield Prediction , 2019, Frontiers in Plant Science.

[13]  Jie Sun,et al.  County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model , 2019, Sensors.

[14]  Kumaraswamy Ponnambalam,et al.  Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia , 2019, Journal of Hydroinformatics.

[15]  Gu-Yeon Wei,et al.  Benchmarking TPU, GPU, and CPU Platforms for Deep Learning , 2019, ArXiv.

[16]  Derek M. Heeren,et al.  Fuzzy control system for variable rate irrigation using remote sensing , 2019, Expert Syst. Appl..

[17]  Yang-Won Lee,et al.  A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006-2015 , 2019, ISPRS Int. J. Geo Inf..

[18]  Andrew C. Parnell,et al.  Convergence in Maximum Stomatal Conductance of C3 Woody Angiosperms in Natural Ecosystems Across Bioclimatic Zones , 2019, Front. Plant Sci..

[19]  Francisco C. Pereira,et al.  Multi-output bus travel time prediction with convolutional LSTM neural network , 2019, Expert Syst. Appl..

[20]  Patrizia Busato,et al.  Machine Learning in Agriculture: A Review , 2018, Sensors.

[21]  Stefano Ermon,et al.  Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data , 2018, COMPASS.

[22]  Chao Liu,et al.  Predicting County Level Corn Yields Using Deep Long Short Term Memory Models , 2018, ArXiv.

[23]  Andrej Ceglar,et al.  Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast , 2018, Scientific Reports.

[24]  Yun Shi,et al.  3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images , 2018, Remote. Sens..

[25]  Baofeng Su,et al.  Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.

[26]  Stefano Ermon,et al.  Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data , 2017, AAAI.

[27]  Yuqi Bai,et al.  Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer , 2017 .

[28]  Ying Li,et al.  Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network , 2017, Remote. Sens..

[29]  Zhi Liu,et al.  3D-based Deep Convolutional Neural Network for action recognition with depth sequences , 2016, Image Vis. Comput..

[30]  Nari Kim,et al.  Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data :A Case of Iowa State , 2016 .

[31]  Meng Wang,et al.  A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition , 2015, International Journal of Computer Vision.

[32]  Ryosuke Shibasaki,et al.  Estimating crop yields with deep learning and remotely sensed data , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[33]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[34]  Sebastian Scherer,et al.  3D Convolutional Neural Networks for landing zone detection from LiDAR , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[35]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[36]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[37]  David M. Johnson.  An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States , 2014 .

[38]  Douglas K. Bolton,et al.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .

[39]  Nuno Carvalhais,et al.  Estimating air surface temperature in Portugal using MODIS LST data , 2012 .

[40]  Josh Lofton,et al.  Estimating Sugarcane Yield Potential Using an In-Season Determination of Normalized Difference Vegetative Index , 2012, Sensors.

[41]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[43]  J. Gallego,et al.  Accuracy, Objectivity and Efficiency of Remote Sensing for Agricultural Statistics , 2010 .

[44]  Benoît Duchemin,et al.  A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index , 2008, Environ. Model. Softw..

[45]  Inchi Hu,et al.  BAYESIAN STOCHASTIC ESTIMATION OF THE MAXIMUM OF A REGRESSION FUNCTION , 2006 .

[46]  Jeffrey W. White,et al.  From genome to crop: integration through simulation modeling , 2004 .

[47]  Jürgen Schmidhuber,et al.  Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[48]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[49]  Lahcen Koutti,et al.  MultiD-CNN: A multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences , 2020, Expert Syst. Appl..

[50]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[51]  Z. Wan.  Collection-5 MODIS Land Surface Temperature Products Users' Guide , 2006 .