# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 4 Deep Learning in Plant Water Stress Assessment
### Soil Water Modelling  
Soil water content can be affected by many factors including soil properties, climatic changes, plant growth dynamics, etc., hence it is challenging to determine the soil water content variation accurately and in a timely manner. Modelling the soil water distribution using collected data can be beneficial for predicting in advance the moisture condition in soil by a few days earlier. This will not only support in planning an appropriate irrigation schedule, but can also reduce the impact of drought conditions [63]. However, modelling soil water distribution is not an easy task due to the complex hydrological nature of soil and the correlation with plant and environmental variables. Efficient algorithms are necessary to deal with the nonlinear complex characteristics.  
DL has played an important and increasing role in soil water modelling using soil, plant and environmental measurements and has shown better performance than the conventional ML model. [PERSON] et al. [64] proposed DBN as feature extraction method combined with the macroscopic cellular automata (MCA) model to simulate spatio-temporal soil hydrological changes in an irrigated corn field using several determining environmental parameters. In comparison to conventional Multilayer Perceptron (MLP), DBN has shown better results for predicting soil water content data, with a reduced error of 18%.  
[PERSON] et al. [65] used a DNN-based regression model to predict soil moisture at a depth of 20 cm using selected meteorological variables and soil water content data. Comparison with conventional models such as linear regression, support vector machine (SVM), and ANN showed a higher correlation in DNN with the actual data.  
[PERSON] et al. [66] proposed a hybrid convolutional neural network-gated recurrent unit (CNN-GRU) for predicting soil moisture in maize root zone using input from soil moisture content and meteorological variables from five different cultivation areas. The validation results showed that CNN-RGU performs better than the CNN and GRU model alone. In an extended forecasting period, the predicted soil moisture from the proposed models were comparable to the sensor-based soil moisture measurements. In another study, [PERSON] et al. [67] investigated further the application of DL models and proposed a combination of a CNN-based Resnet and Bidirectional Long Short-Term Memory (BiLSTM) model for soil water content prediction at four different depths using integrated meteorological, soil water content and growth stage records data for training. The deep learning model had better prediction accuracy than the traditional machine learning models of support vector regression (SVR), MLP and random forest (RF).  
DL has also been used for large scale soil moisture prediction based on satellite data such as SMAP. [PERSON] et al. [68] developed LSTM that predicts SMAP level-3 moisture product with atmospheric forcing, model-simulated moisture, and static physiographic attributes as inputs. The results showed that LSTM outperformed conventional methods of multiple linear regression (MLR), autoregressive models (AM) and one-layer ANN. In a more recent study, [PERSON] and [PERSON] [69] further improved the work by introducing a novel data integration (DI) kernel to assimilate the most recent SMAP observations for near-real-time forecast of SM product. The DI-LSTM was compared to the original LSTM model and gave less error in the forecasted values compared to actual measurements.