## 1 Introduction
[30], using historical data on soil moisture, rainfall, and meteorological variables, a neural network was trained to predict volumetric soil moisture content one day in advance. Three study sites with land covered in farmland, arable land, and grassland provided the data for building LSTM models to predict soil moisture. An \(R^{2}\)-score greater than 0.94 was achieved in the model evaluation at each test site. [PERSON] et al. [31] proposed the use of two nonlinear regression models, Multilayer Perceptron (MLP) and support vector regression, to estimate vineyard soil moisture from digital photographs. Both models were trained using pixels encoded with the RGB color model extracted from digital photographs of the soil, combined with the associated known soil moisture levels, to predict moisture content from newly acquired images. In the analysis of the test data, both approaches were successful in predicting soil moisture and provided good correlation coefficients between observed and predicted soil moisture. An LSTM model was employed by [PERSON] et al. [32] to predict groundwater level rise in agriculture. They used 14 years of time series data, including information on water table depth, temperature, reference evapotranspiration, rainfall, and water diversion. In terms of forecasting the depth of the water table, the proposed model outperformed the Feed-Forward Neural Network (FFNN) model in terms of the \(R^{2}\)-score. In other research, the timing and volume of irrigation are directly predicted. A Deep Q-learning (DQN) model was applied by [PERSON] et al. [33] to a rice irrigation decision strategy based on short-term weather forecasts. Daily recorded weather information from three stations during the rice growing season was used as the input of the model. The DQNirrigation approach showed a significant reduction in irrigation water volume, irrigation time, and drainage water without yield loss compared to the results of the conventional irrigation strategy. The DQN model was used by [PERSON] et al. [34] to schedule the irrigation of a tomato crop in Portugal using climatic Big Data. Soil moisture estimation was used as the state of the environment of the agent. The agent decided when and how much to irrigate based on the predicted next state of the environment (estimate of soil moisture and some climate factors for the next day). Compared to fixed and threshold irrigation, the DQN model showed a reduction in irrigation water volume without affecting production. Soil water-holding capacity is a key factor in crop growth and yield and plays a critical role in Mediterranean rain-fed crops. Plant available soil water at a given rooting depth can be estimated from the Fraction of Transpirable Soil Water (FTSW) calculated as the ratio between the Available Soil Water (ASW) and Total Transpirable Soil Water (TTSW) [35; 36]. The strong and stable relationship between the FTSW and vine leaf water potential allows its use as an indicator of soil water deficit in vineyards [35]. [PERSON] et al. [37] used a soil-plant-atmosphere system composed of simply defined subsystems and a geometric vine canopy model for estimating the FTSW in the Alsace region, France. Simulations were performed over four seasons and compared with the FTSW data measured with a neutron probe. In general, the model provided a good estimate of the evolution of the FTSW, but it was very sensitive to most canopy parameters and depended on their accurate determination or simulation. In Alentejo, southern Portugal, [PERSON] et al. [38] used the FTSW to study the effects of vineyard soil management practices combined with deficit irrigation strategies on the vine water status and performance of the Tempranillo grape variety. The study showed that the FTSW is a strong indicator of plant water status, enabling the discrimination of the effects of soil management practices on water use. [PERSON] et al. [39] used the Pedo-Transfer Function approach (PTF) to estimate Plant Available Water Capacity (PAWC) in the Barosa region, South Australia.