# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 4 Deep Learning in Plant Water Stress Assessment
### Plant Water Status Estimation  
Many techniques have been developed over the years to describe plant water status (PWS) such as relative water content (RWC) [89], equivalent water thickness (EWT) and fuel moisture content (FWC) [90]. Other techniques are based on the changes in the plant physical system as a response to water content, such as stem diameter variations (SD) [91; 92]. These parameters have been used as indicators for assessing plant conditions related to drought (as water stress index) and irrigation management [93]. Recent progress has seen remote sensing measurement estimate plant water status non-destructively with various modelling methods have used to correlate the measurement and ground measured plant water status, ranging from simple linear regression to machine learning technique [94; 95; 96].  
The DL model is currently being investigated as a fast and robust method for PWS estimation using aggregated data collected from various sensory measurements. [PERSON] et al. [97] evaluated CNN and the Random forest (RF) technique as regression model to estimate RWC in plant leaf from the transmission coefficient of frequency data collected using Non-contact resonant ultrasound spectroscopy (NC-RUS). The CNN technique utilizes the entire ultrasonic spectra collected non-invasively from the leaf while RF takes only four relevant input spectra resulting in better performance of the CNN model as shown by the higher correlation and lower median prediction error.  
DL has also been used to extract features from multi-modal data to increase inversion performance. [PERSON] et al. [98] combined environmental measurements with leaf wilting features extracted from plant images using a CNN-based model as multi-modal feature extractor to predict changes in stem diameter to assess water status in the tomato plant. Comparison of several conventional regression algorithms showed the superiority of the proposed model due to its low prediction error. The method proposed, however, has not considered the temporal aspect of the data which could further increase the prediction accuracy. In more recent years, [PERSON] et al. [99] improved the existing work by proposing an LSTM network with clustering-based drop (C-Drop). The C-Drop neural network supports the regression analysis by giving the environmental feature an equal consideration. Based on the results, the new proposed model improved the prediction accuracy by 21% compared to the previous method.  
Plant spectral information has been used to estimate PWS based on the developed vegetation indices techniques, such as WI and NDWI [100]. Moreover, ML has been widely investigated as the method used in the process of finding the relevant spectral bands that give high correlation with the PWS parameters [90; 101; 102; 103; 104]. DL, however, has been hardly applied to this problem. [PERSON] et al. [105] were possibly the first to have used a 1D-convolutional neural network to extract the mean spectral reflectance from the hyperspectral image to predict RWC in maize. The model based on the updated Inception module called DeepRWC was able to learn the features from hyperspectral data without the need for spectral selection or dimensionality reduction. DeepRWC achieved better accuracy in comparison with the two standard approaches of partial least square regression (PLSR) and SVM.  
In another study, [PERSON] et al. [106] have integrated a physical model with RNN to accurately predict live moisture fuel content (LMFC), defined as the mass of canopy leaf water per unit of dry biomass, which is a key parameter for assessing wildfire risk. The model takes input variables from microwave remote sensing of Sentinel-1 backscatter and Landsat-8 optical reflectance, with additional soil properties, for training on LFMC field samples. They found that the DL model performance exceeds the conventional process-based methods and that precision can be improved with the addition of the microwave RS data.