This study was conducted to evaluate the laboratory tolerance of the durum wheat cultivar (Douma 3) when treated with two levels of ascorbic acid (5 ppm and 10 ppm, in addition to a control treatment soaked in water) under two levels of salt stress (50 mM and 100 mM NaCl, in addition to a control). The experiment took place at the Field Crops Department labs, Faculty of Agricultural Engineering, University of Damascus, during the 2022–2023 agricultural season. The aim was to study the effect of ascorbic acid on seed reserve utilization efficiency, peroxidase enzyme activity, and its role in salt stress tolerance. The experiment followed a randomized complete block design (RCBD) using factorial ANOVA with two replicates. The results showed significant differences between the treatments, with the priming of seeds soaked in a 5 ppm ascorbic acid solution (A1) significantly outperforming in terms of seedling dry weight (22.67 mg/seedling), remaining seed dry weight (7.5 mg/seed), seed reserve utilization efficiency (0.47 mg/mg), and salt tolerance index (89.80%). Simple correlation analysis showed a significant positive correlation between seedling dry weight (SDW), seed reserve utilization efficiency (SRUE) (0.881), and salt tolerance index (STI) (0.746 *). However, a negative and non-significant relationship was observed between the remaining seed dry weight (RSDW) and other traits. Moreover, SRUE had a significant positive correlation with STI (0.814). Both total soluble protein concentrations and peroxidase enzyme activity increased under salt stress conditions following pre-treatment with ascorbic acid compared to the control. The highest protein concentration and peroxidase enzyme activity were observed with the 5 ppm ascorbic acid treatment (A1).

Introduction: Accurate prediction of soil moisture content (SMC) is crucial for agricultural systems as it affects hydrological cycles, crop growth, and resource management. Considering the challenges with prediction accuracy and determining the effect of soil texture, depth, and meteorological data on SMC variation and prediction capability of the used models, this research has been conducted. Methods: Three machine learning (ML) models—random forest regression (RFR), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)—were developed to predict SMC in three soil types (loam, sandy loam, and silt loam) at five depths of 5, 20, 40, 60, and 80 cm. The dataset was collected during the maize season in 2023, encompassing meteorological parameters collected using Internet of Things (IoT)-based sensors and SMC data calculated using the gravimetric method. Results: The results showed variations in SMC in all studied soil types and depths, with silt loam exhibiting the highest variation in SMC. RFR demonstrated high accuracy at different depths and soil types, particularly in loam soil, at a depth of 80 with a root mean square error (RMSE) value of 0.89 and a mean absolute error (MAE) value of 0.74, and in silt loam at 40 cm depth with an RMSE value of 0.498 and an MAE of 0.416. LSTM performed effectively at shallower and moderate depths (60 and 20 cm) with RMSE values of 0.391 and 0.804 and MAE values of 0.335 and 0.793, respectively. In sandy loam soil at 5 cm depth, XGBoost displayed minimal errors and robust performance at the same depths with higher accuracy, achieving an RMSE of 0.025 and an MAE of 0.159. Analysis of training and validation loss revealed that the LSTM model stabilized and improved with more epochs, showing a more consistent decrease in MSE, while RFR and XGBoost exhibited higher performance with increased model complexity, shown in low MSE and RMSE values. Comparisons between measured and predicted SMC% values demonstrated the models’ effectiveness in capturing soil moisture dynamics. Furthermore, feature importance analysis revealed that solar radiation and precipitation were the most influential predictors across all models, offering critical insights into dominant environmental drivers of soil moisture variability. Discussion: By providing precise SMC predictions across different spatial and temporal scales, this study underscores the value of ML models for SMC prediction, which could have implications for improving irrigation scheduling, reducing water wastages, and enhancing sustainability.