# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 1 Introduction  
Water stress, also known as drought stress, is part of plant abiotic stress, a pressing threat to plant productivity if sustained over a long period [1]. Most major crop plant yields can reduce by more than half due to lack of water availability [2; 3]. Failure to produce agriculture will in turn create a food security challenge, affecting the nation's economy as well as threatening farmers' livelihood. Routine plant water stress assessment could help minimize the risk of productivity loss by timely detection and appropriate intervention. On the other hand, moderate stress induced in plants can improve the quality of yields as supplying excess irrigation can lead to loss of nutrients in soil [4]. A moderate level of water stress applied during the suitable growth period of mandarin and peach cultivation can benefit through enhanced fruit quality parameters without significantly compromising yield [5]. Regulated deficit irrigation (RDI) is the key technology that can help improve plant water use efficiency, at the same time reducing the staggering amount of agricultural water utilization [6]. Knowledge of plant water stress assessment methods istherefore highly significant for precision control of irrigation as not to induce unrecoverable stress [7]. Moreover, to some extent, assessing the water status in plants could even prevent wildfire [8], help in monitoring infested trees which inhibit water stress like symptoms [9] and become a valuable tool for water stress resistant plant breeding [10]. There are many factors that can inflict water stress in plants. Soil condition, in particular the moisture level, is directly correlated with the available water for plant consumption. Water stress occurs as a result of an imbalance between water availability and water consumption which leads to soil moisture being the widely used indicator of water stress in plants [11; 12]. Environmental factors such as air temperature, relative humidity and solar radiation can influence plant transpiration and soil water evaporation resulting in low water content in plants. Hence several methods of plant water stress assessment are based on ecological measurements [13]. Recently, plant physiological responses to limited water conditions has been used as indicator of stress and is considered more sensitive than soil moisture [14]. [PERSON] [15] has argued that plant water stress assessment should be performed directly on the plant, as growth is directly affected by the tissue water condition and only indirectly by soil water deficit. However, most of the techniques used to measure the responses in plants are destructive, time consuming, and labour intensive. Advances in sensing technologies have truly revolutionized the methods for plant water stress assessment as illustrated in Figure 1, allowing rapid, automated and cost-efficient soil-plant echophysiology monitoring [16]. These days, environmental sensors have become inexpensive owing to the popularity of IoT devices that can measure agricultural data in real-time. Various state-of-the-art remote sensing (RS) techniques have also been introduced for fast and non-invasive plant water status estimation [17]. [PERSON] and [PERSON] [18] have reported in the recent literature on several RS approaches for plant water stress assessment in the cultivated environment for irrigation management. Numerous platforms and sensor types have been used providing unlimited heterogenous data, analysed using a range of modelling approaches. Machine learning (ML) has promptly become the standard for data processing especially in agriculture, thanks to its ability of to process large amounts of information in a non-linear framework [19; 20]. Despite the significant achievements of ML application, the technique has a fundamental limitation in that performance is subject to the features used, the quality of data collected and the specific targeted application [21].