# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 4 Deep Learning in Plant Water Stress Assessment
### Plant Water Stress Identification  
Plant water stress identification refers to water stress detection in plants by distinguishing water stress and non-water stress in the labelled plant. A computer vision system for plant water stress identification has been developed over the years using conventional ML algorithms such as ANN [107], Adaptive neuro-fuzzy classifier [108], and Gradientboosting decision tree (GBDT) [109]. However, the need for feature extraction from the images and for selection of conventional ML has limited the performance of the analysis. DL superiority in image recognition by automatically learning from patterns has been leveraged in plant water stress identification, with CNN becoming the standard model for automated feature extraction and transformation. [PERSON] et al. [110] were possibly the first who attempted to identify plant water stress in maize using pre-trained CNN: Resnet50 and Resnet120 based on three treatments of stress: optimum moisture, light drought, and moderate drought stress. The performances of the models showed between 91-98% accuracy with the fastest training time close to 8 min. Compared to manually extracted features and classification using the conventional GBDT model, CNN performs better with Resnet50 showing the highest accuracy. [PERSON] et al. [111] attempted to improve the performance of the identification by introducing a Gabor filter to extract texture features from the same dataset used in [110] for the Zhengdan maize variety and reduced the dimension before feeding to the pre-trained CNN model of Alexnet. The results showed slight improvement in the model accuracy but with good adaptation to illumination changes and angle transformation. [PERSON] et al. [112] further investigated the Zhengdan maize dataset by designing their own CNN architecture, that was relatively simple but efficient in feature extraction, by selecting the most relevant feature maps. The proposed method used SVM as final classifier and the results showed better accuracy achieved in comparison with the other well-known CNN models of VGG16, ResNet50, Xception and xPLNet. [PERSON] et al. [113] evaluated instead the three different popular CNN models of AlexNet, GoogLeNet and Inception V3 for plant water stress classification of maize, okra and soybean images collected from different growth stages. The performance of GoogLeNet was found to be superior compared to others with an accuracy of 98.3%, 97.5% and 94.1% for maize, okra and soybean, respectively. Realizing the potential of CNN as an image classifier for plant water stress identification, recent studies have been assessing the applicability of such techniques to be used in field conditions. Plant images were collected at certain heights providing wide monitoring of multiple plants under the same cultivation area. [PERSON] et al. [114] used the pre-trained CNN of VGG16 for real time classification of water stress treatment of five different groups of corn. The proposed method used the image data concatenated with the plant images as an input to the DL model. The results showed excellent accuracy for five treatment classifications, although most studies showed less performance when dealing with a higher number of classes. [PERSON], et al. [115] assessed the use of cloud-based CNN training to identify plant water stress across six container-grown ornamental shrub species. Using near infrared images captured in the field from small, unmanned aircraft system (sUAS), the framework was able to achieve high accuracy performance despite constraints of small sample size, low image resolution, and lack of clear visual differences. Although CNN is the most commonly used model in image processing, the sequential characteristic of the time-series image is not considered in the previous classification tasks. The temporal dependence relationship of a specific plant growth period may hold information that can be used for water stress identification. In consideration of the temporal feature, [PERSON] et al.