###### Abstract
To address these concerns, a decision support system has been developed [20]. It is employed to help decision makers gather useful information to identify problems and formulate optimized strategies [21]. The simplest decision system implements automatic irrigation based on the soil volumetric water content data obtained from a real-time monitoring capacitive soil water sensor [22; 23]. Irrigation is started or stopped when the sensor measurement value is below or above the predetermined threshold value [24; 25]. Some researchers have used the decision support system to implement irrigation strategy which demonstrated regulated deficit irrigation could be achieved without human intervention [26]. However, one drawback of this approach is the lag in the soil moisture response. Since it takes time for the irrigated water to seep through the soil profile, the amount of irrigation may exceed the expected value by the time the soil moisture sensor reaches the upper limit set to terminate irrigation. Thus, the solution of this study is to improve the current decision support system by establishing upper and lower irrigation thresholds, calculating the amount of irrigation needed for soil moisture to reach the upper limit from the set lower limit, and automatically terminate the system processing when the irrigation reaches the set amount after initiation. With the development of remote sensing technology, solar-induced fluorescence (SIF) offers unique opportunities for environmental stress (drought and high temperature) monitoring and estimation of gross primary productivity (GPP). Several studies have explored the application of SIF. For example, [PERSON] et al. [27] used SIF and Enhanced Vegetation Index (EVI) to detect the effects of drought in Amazon forest, and they showed that SIF showed a significant decrease in the central Amazon forest during the dry season, while the change in EVI was not significant. [PERSON] et al. [28] explored the response of SIF and the NDVI of winter wheat to different drought intensities. They found that SIF was significantly reduced under severe and extreme drought conditions, while the NDVI was significantly reduced only under extreme drought. Moreover, the results showed that SIF was significantly and positively correlated with soil moisture, indicating that SIF can capture agricultural drought information based on the value of soil moisture. The above results suggest that SIF is more suitable for agricultural drought monitoring. [PERSON] et al. [29] used SIF, the NDVI and EVI to monitor the effects of high temperature stress on wheat in Northwestern India. The results showed that satellite SIF has great potential for timely monitoring of heat stress and large-scale assessment of its impact on wheat yield. Since SIF contains information on plant physiological, biochemical and metabolic properties, it is considered a suitable proxy for vegetation photosynthesis. Previous studies have found that SIF can accurately estimate GPP in different ecosystems and that SIF can also capture changes in GPP due to drought in a timely manner. In the context of climate change, drought is occurring more and more frequently, and in order to reduce the impact of drought on agriculture, real-time drought monitoring is essential. Currently, drought indices, meteorological parameters and vegetation indices (VIs) are commonly used for drought monitoring [30]. However, they are all inadequate in monitoring the physiological changes of vegetation caused by drought. VIs based on surface reflectance, are more sensitive to the greenness of vegetation. However, the vegetation biomass and canopy structure are not the result of instantaneous photosynthesis, but the photosynthetic yield accumulated over time, so the VIs may not respond to drought in a timely manner [31]. On the other hand, drought indices such as standardized precipitation evapotranspiration index (SPEI) and Palmer drought severity index (PDSI) are calculated using surface water balance based on observed precipitation and temperature data [32].