# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 4 Deep Learning in Plant Water Stress Assessment
### Soil Moisture Estimation  
Soil moisture (SM) is an important parameter for assessing plant water stress as it is directly related to water availability for plant consumption. Over the years, SM measurement has been used for agricultural drought monitoring and irrigation control [55]. It is critical that the percentage of SM does not approach the permanent wilting point, which is the lower limit of plant-available water [56]. The traditional soil sampling few points method for moisture analysis is time-consuming and does not reflect the whole field conditions accurately. In situ measurement, on the other hand, is not feasible for large field application due to the high cost of sensor installation [14]. Advanced remote sensing technique has  
Figure 4: Deep long short-term memory network training architecture. (**a**) Description of a structure of a single Long Short-Term Memory (LSTM) cell; (**b**) Basic deep LSTM structure typically constructed of multiple LSTM cells.  
offered advantages in terms of low-cost and wide scale of monitoring. DL techniques have been used for fast and accurate SM estimation from several remote sensing platforms.  
Several studies have used DL for aerial image analysis which could be easily captured using an unmanned aerial vehicle (UAV) or drone for SM estimation. [PERSON] et al. [57] proposed a CNN-based regression model to estimate SM content from aerial captured thermal images from three different farm areas. The model was able to predict SM content more accurately than the plain DNN model. The technique shows promising application; however, there is currently a lack of adequate local ground measurement data available at the scale of captured images due to the cost and labour. [PERSON] et al. [58] tried to overcome the shortcoming by developing an image simulator that can generate pseudo real plant images from the available SM dissipation rate data. The study proposed CNN, which has generated less error in the prediction of soil moisture dissipation rate from the simulated test images in comparison to the traditional ML methods. It also worth noting that the CNN method was robust against the noise introduced into the simulated images.  
Large scale SM monitoring has been explored based on satellite observation. DL has been proposed as an alternative to conventional physically-based models developed for SM estimation using satellite data which often perform less well due to limited processing capacity. [PERSON] et al. [59] used DNN for upscaling in-situ soil moisture estimation using Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR). The DL model was able to achieve better accuracy than Soil Moisture Active Passive (SMAP) active sensor products and the Global Land Data Assimilation System (GLDAS) model. In another study, [PERSON] et al. [60] also employed DNN to estimate soil moisture over the Korean peninsula using surface and thermal variables from satellite. Compared with Advanced Microwave Scanning Radiometer-2 (AMSR\({}^{2}\)) and GLDAS SM products, the model showed excellent agreement with the ground measured data.  
[PERSON] et al. [61] used DBN to extract features from Fengyun-3D (FY-3D) Medium Resolution Spectral Imager-II (MERSI-II) imagery to estimate soil moisture in the Ningxia Hui Autonomous Region of China. The developed model, called SM-DBN, has outperformed the other conventional models of linear regression (LR) and ANN, based on accuracy performance in correlation with the actual ground measurement data. [PERSON] et al. [62] compared the performance of CNN and conventional ANN for estimating in-situ SM using satellite data of L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and soil temperature. The results showed that CNN outperformed conventional ANN with better correlation with in-situ soil moisture measurement.