  Computational tools for forecasting yields and prices for fresh produce have
been based on traditional machine learning approaches or time series modelling.
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 data
of SantaBarbara as input and the corresponding strawberry yields or prices as
output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which
is an ensemble model of Convolutional NeuralNetwork LSTM (CNN-LSTM) models, is
trained and tested using satellite images of the same county as input mapped to
the same yields and prices as output. These two ensembles forecast strawberry
yields and prices with minimal forecasting errors and highest model correlation
for five weeks ahead forecasts.Finally, the forecasts of these two models are
ensembled to have a final forecasted value for yields and prices by introducing
a voting ensemble. Based on an aggregated performance measure (AGM), it is
found that this voting ensemble not only enhances the forecasting performance
by 5% compared to its best performing component model but also outperforms the
Deep Learning (DL) ensemble model found in literature by 33% for forecasting
yields and 21% for forecasting prices
