# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 4 Deep Learning in Plant Water Stress Assessment
### Enaptranspiration Estimation  
Evaptranspiration (ET) correspond to atmospheric water loss due to plant transpiration and soil evaporation. Crop evapotranspiration (ETc) specifically provides understanding of how fluctuations in climatic parameters can affect plant water consumption under the active phase without restriction from nutritional supply [70]. Measuring field ETc conventionally using a lysimeter is expensive, complex and labour-costly [71]. The FAO-56 [72] model is the well-established method for ETc calculation based on energy and water balance schemes at field scale shown in Equation (1):  
\[\text{ETc}=\text{Kc}\times\text{ETo}, \tag{1}\]  
where Kc is the crop coefficient and ETo is the reference evapotranspiration. ETo incorporates the effects of weather into the ETc estimate whilst the properties of the crop which affect ETc are quantified by Kc [73]. ETo is traditionally calculated using the Penman-Montieth (PM) Equation [72] based on measured meteorological sensors. However, the method is limited to the need for large variables. Deep learning has been used for faster estimation of ETo with limited meteorological variables as inputs. [PERSON] and [PERSON] [74] used optimized meteorological based processed data as input to DNN for estimation of evapotranspiration values in Punjab, Northern India. Comparison with the standard methods of Generalized Linear Model (GLM), RF, and Gradient-Boosting Machine (GBM) showed DNN's better performance, providing higher accuracy with the PM-based ETo while avoiding the overfitting issue. In another study, [PERSON] and [PERSON] [75] proposed that the CNN model showed good performance when used to estimate daily ETo directly from limited hourly meteorological data compared to other models such as RF, extreme gradient boosting (XGBoost), and ANN. [PERSON] et al. [76] used LSTM and BiLSTM for estimating ETo using air temperature and relative humidity as the only variables. Both DL models showed high accuracy compared to the actual ETo, with less difference in performance between the two models. [PERSON] et al. [77] evaluate the performance of three DL methods of deep DNN, temporal convolution network (TCN) [78], and long short-term memory neural network (LSTM) for reference evapotranspiration estimation. The results showed that DL model outperformed other conventional models, and TCN and LSTM showed outstanding performance when temperature features were used. Recently, remote sensing has been used for large scale ET estimation providing high spatial and temporal resolution. Several models and algorithms, such as Mapping evapotranspiration at high resolution with internalized calibration (METRIC), the Two-source energy balance (TSEB) model, and machine learning have been used and discussed [79]. Recently, DL learning has been introduced to improve the ET estimation based on remotely sensed data. [PERSON] et al. [80] developed a gap-filling algorithm based on the temperature surface-vegetation index (TSVI) model to provide continuous daily actual ET estimation at regional scale. Results showed good correlation between the estimated ET from TSVI-DNN model with ground observation. [PERSON], et al. [81], for the first time, introduced CNN for estimating spatially distributed ET using remote sensing data without the need of a surface energy model. Comparison with data from METRIC showed satisfactory ET maps were produced by the CNN. At individual plant scale, stress variation is expressed directly in plant transpiration (T). Measuring plant transpiration can give better understanding of the relation between plant stress and the environment. However, direct measurement of T by sap flow is expensive, difficult and time-consuming. Several studies have used DL for plant transpiration estimation using a sensory measured environmental variable. [PERSON] et al. [82] have utilized DBN and least square support vector machine (LSSVM) to directly predict the leaf transpiration rate of strawberry in a greenhouse environment.