In this survey paper, we review different techniques for document understanding for documents written in english and consolidatemethodologies present in literature to act as a jumping-off point forresearchers exploring this area.
This report presents a brief survey on development of deep learning (dl) approaches, including deep neural network (dnn), convolutional neural network (cnn), recurrent neural network (rnn) including long short term memory (lstm) and gated recurrent units (gru), auto-encoder (ae), deep belief network (dbn), and generative adversarial network (gan).
In addition, we have included recent development of proposed advanced variant dltechniques based on the mentioned dl approaches.
We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in santa barbara county, california.
Building the proposed forecasting model comprises three stages : first, the station-based ensemble model (att-cnn-lstm-seriesnet_ens) with its compound deep learning components, seriesnet with gated recurrent unit (gru) and convolutional neural network lstm with attention layer (att-cnn-lstm), are trained and tested using the station-based soil temperature and moisture dataof santabarbara as input and the corresponding strawberry yields or prices as output.