# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 5 Discussion and Future Perspectives  
Deep learning is a powerful and versatile tool that can be applied to solve a wide range of problems either through regression or classification analysis. The superior performance compared to conventional ML makes it a suitable method for modelling highly complex plant water stress conditions. Remote sensing is another advanced method with much potential in plant water stress assessment, providing high spatial, temporal and spectral resolution. However, it has been noted that DL application in remote sensing plant water stress assessment is still at the initial stage of development. This is especially true in the case of EIC estimation. Many strategies can still be explored and a few studies have started to pave the way in utilizing DL for solving problems in the remote sensing application in reference to ET estimation [80,81,117].  
Remote sensing thermal and hyperspectral images have been extensively investigated for plant water stress assessment based on vegetation indices and the crop water stress index technique. However, challenges remain regarding the effect of plant geometric structure and image background which could influence the analysis. RGB image has been proposed for remote sensing estimation of plant water content estimation utilizing morphological features (Leaf Area) [118,119] and colour [120] to estimate plant physiological response to water stress. It is preferable due to the low-cost and easy to use sensor [120]. DL has been successfully used for segmenting plant and background in RGB images for plant water stress detection with good results [121]. However, lack of spectral bands, with only three basic colours in the image, limits detailed information about the status of the plant [122]. [PERSON] et al. [123] proposed an innovative approach to using DL for predicting vegetation  
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**Deep Learning (DL) Applications**  
index from an RGB aerial image. The development shows that it is possible to have a low-cost and efficient method for plant water stress assessment while improving agricultural production with the help of an advanced algorithm such as DL.  
The inherent bottleneck of the DL technique has always been the size and quality of the datasets available for training and validating the model. Data collected through remote sensing mostly involves images, and although satellite-based images are commercially available, data on aerial plant images under water stress is limited leading most of the studies to use self-collected images while adopting transfer learning in their application. Transfer learning is a method of using a pre-trained model for other applications which can be used to train a new model from scratch using available models. It has already been successfully used for solving the problem of small sample size with improved accuracy. For a robust model to be developed, large varieties of datasets are required including data collected from different sensors and plant species.  
Despite successful applications and superior performance achieved relative to conventional ML methods, DL utilization in water stress assessment is still at the beginning phase and there is still a lot to explore. Some of the future prospects are discussed together with the possible key issues.