Methodology for multi-temporal prediction of crop rotations using recurrent neural networks

[1]  B. Agard,et al.  Performances of a Seq2Seq-LSTM methodology to predict crop rotations in Québec , 2023, Smart Agricultural Technology.

[2]  B. Agard,et al.  Predicting crop rotations using process mining techniques and Markov principals , 2022, Comput. Electron. Agric..

[3]  Ranjita Das,et al.  RNN Encoder And Decoder With Teacher Forcing Attention Mechanism for Abstractive Summarization , 2021, 2021 IEEE 18th India Council International Conference (INDICON).

[4]  Shun-Chi Wu,et al.  Abnormal event detection, identification and isolation in nuclear power plants using LSTM networks , 2021 .

[5]  Wei Lu,et al.  Mixed Cross Entropy Loss for Neural Machine Translation , 2021, ICML.

[6]  K. E. ArunKumar,et al.  Forecasting of COVID-19 using deep layer Recurrent Neural Networks ( RNNs ) with Gated Recurrent Units ( GRUs ) and Long Short-Term Memory ( LSTM ) cells , 2022 .

[7]  Koutilya PNVR,et al.  Pre-season crop type mapping using deep neural networks , 2020, Comput. Electron. Agric..

[8]  Fangzhou Xu,et al.  Scalp EEG classification using deep Bi-LSTM network for seizure detection , 2020, Comput. Biol. Medicine.

[9]  Mustafa Baysal,et al.  Deep learning methods and applications for electrical power systems: A comprehensive review , 2020, International Journal of Energy Research.

[10]  Zabir Al Nazi,et al.  Classification of ECG signals by dot Residual LSTM Network with data augmentation for anomaly detection , 2019, 2019 22nd International Conference on Computer and Information Technology (ICCIT).

[11]  Ying Ma Seed coating with beneficial microorganisms for precision agriculture. , 2019, Biotechnology advances.

[12]  Chen Zhang,et al.  Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps , 2019, Comput. Electron. Agric..

[13]  Weihua Ou,et al.  Sentiment Analysis of Text Based on Bidirectional LSTM With Multi-Head Attention , 2019, IEEE Access.

[14]  Yu Liu,et al.  Potential of artificial grasslands in crop rotation for improving farmland soil quality , 2019, Land Degradation & Development.

[15]  Tariq S. Durrani,et al.  Exploring Trajectory Prediction Through Machine Learning Methods , 2019, IEEE Access.

[16]  J. Isselstein,et al.  Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data , 2019, Land.

[17]  M. de Rijke,et al.  Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss , 2019, WWW.

[18]  Hongxiao Fei,et al.  Bidirectional Grid Long Short-Term Memory (BiGridLSTM): A Method to Address Context-Sensitivity and Vanishing Gradient , 2018, Algorithms.

[19]  Michael Granitzer,et al.  Sequence classification for credit-card fraud detection , 2018, Expert Syst. Appl..

[20]  Naren Ramakrishnan,et al.  Deep Reinforcement Learning for Sequence-to-Sequence Models , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Chung Choo Chung,et al.  Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[22]  Richard Socher,et al.  A Flexible Approach to Automated RNN Architecture Generation , 2017, ICLR.

[23]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[24]  Jiang Qian,et al.  Text sentiment analysis based on long short-term memory , 2016, 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI).

[25]  Bowen Zhou,et al.  Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond , 2016, CoNLL.

[26]  Jordi Inglada,et al.  Assessment of a Markov logic model of crop rotations for early crop mapping , 2015, Comput. Electron. Agric..

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

[28]  Chris Murphy,et al.  APSIM - Evolution towards a new generation of agricultural systems simulation , 2014, Environ. Model. Softw..

[29]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[30]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[31]  D. Tilman,et al.  Global food demand and the sustainable intensification of agriculture , 2011, Proceedings of the National Academy of Sciences.

[32]  Stephan Dabbert,et al.  Generating crop sequences in land-use models using maximum entropy and Markov chains , 2011 .

[33]  N. H. Ravindranath,et al.  The top 100 questions of importance to the future of global agriculture , 2010 .

[34]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[35]  Nina K. Detlefsen,et al.  Modelling optimal crop sequences using network flows , 2007 .

[36]  Peter Zander,et al.  ROTOR, a tool for generating and evaluating crop rotations for organic farming systems , 2007 .

[37]  Jean-François Mari,et al.  Studying crop sequences with CarrotAge, a HMM-based data mining software , 2006 .

[38]  W. K. Klein Haneveld,et al.  Crop succession requirements in agricultural production planning , 2004, Eur. J. Oper. Res..

[39]  M. K. van Ittersum,et al.  ROTAT, a tool for systematically generating crop rotations , 2003 .

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

[41]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[42]  Amir Mosavi,et al.  Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis , 2020, IEEE Access.

[43]  Mohit Dua,et al.  An Improved RNN-LSTM based Novel Approach for Sheet Music Generation , 2020 .

[44]  M. Mubeen,et al.  Fundamentals of Crop Rotation in Agronomic Management , 2019, Agronomic Crops.

[45]  H. Steinmann,et al.  Identifying crop rotation practice by the typification of crop sequence patterns for arable farming systems – A case study from Central Europe , 2018 .