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Advisor(s)
Abstract(s)
In recent years, deep learning algorithms have been successfully applied in the development
of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases,
weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as
lack of natural resources and climate change, an efficient decision support system for irrigation
is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation
scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM
(BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The
application of these techniques to predict these parameters was tested for three sites in Portugal.
A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine
the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved
the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to
0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential
performance improvement. Performance dropped in all datasets due to the complexity of the
model. The performance of the models was also compared with CNN, traditional machine learning
algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance.
Finally, the impact of the loss function on the performance of the proposed models was investigated.
The model with the mean square error as loss function performed better than the model with other
loss functions.
Description
Keywords
Agriculture Deep learning LSTM Support decision-making algorithms Irrigation management