Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (p < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R² values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives.

Agricultural big data and high-performance computing have significantly improved crop yield modeling. Maize growth dynamics and yield prediction are crucial for sustainable agriculture. This study introduces an advanced modeling approach utilizing Gradient Boosted Decision Trees (GBDT) combined with a feature selection strategy to predict maize biomass production. A dataset of 200 unique maize plants was observed throughout the vegetation season. Our approach integrates manual measurements, meteorological data, and vegetation indices along with Internet of Things (IoT) field sensors to perform spatio-temporal analysis. Results indicate that maize stalk thickness and height are the most reliable predictors of biomass yield, while environmental variables show minimal impact. The most effective model, period-dependent GBDT, demonstrated superior predictive performance, achieving an average error of 4.39 mm in plant growth predictions. Notably, stalk thickness and height can be estimated six weeks before harvest, while biomass yield two weeks before harvest. This research underscores the potential of machine learning and remote sensing to enhance precision agriculture decision-making.

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).

The aim of this study was to estimate field-grown tomato yield (weight) and quantity of tomatoes using a self-developed robot and digital single lens reflex (DSLR) camera pictures. The authors suggest a new approach to predicting tomato yield that is based on images taken in the field, 3D scanning, and shape. Field pictures were used for tomato segmentation to determine the ripeness of the crop. A convolution neural network (CNN) model using TensorFlow library was devised for the segmentation of tomato berries along with a small robot, which had a 59.3 % F1 score. To enhance the accurate tomato crop model and to estimate the yield later, point cloud imaging was applied using a Ciclops 3D scanner. The best fitting sphere model was generated using the 3D model. The most optimal model was the 3D model, which gave the best representation and provided the weight of the tomatoes with a relative error of 21.90 % and a standard deviation of 17.9665 %. The results indicate a consistent object-based classification of the tomato crop above the plant/row level with an accuracy of 55.33 %, which is better than in-row sampling (images taken by the robot). By comparing the measured and estimated yield, the average difference for DSLR camera images was more favorable at 3.42 kg.

“Feed the global population and regenerate the planet.” The conditions necessary for the implementation of the above commonly used slogan did not exist 10–15 years ago. We did not have access to the information and databases that would have allowed us to increase yields for the purpose of feeding the growing population. While increasingly meeting sustainability requirements and regenerating the Earth. Anthropocentrism, the belief that humans are superior to everything else, benefits humans by exploiting human greed and ignorance, which is a dead end for both individuals and societies. Only humans can ignore the dynamic equilibrium processes of nature and disregard the consequences that adversely affect future generations. Ecocentric agricultural practices have several prerequisites. It is important for the academic sphere to recognize its significance. Another fundamental challenge is the continuous monitoring of the production unit and its close and distant environment for the purpose of decision preparation using Big Data. The Internet of Things (IoT) is a global infrastructure that represents the network of physical (sensors) and virtual (reality) “things” through interoperable communication protocols. This allows devices to connect and communicate using cloud computing and artificial intelligence, contributing to the integrated optimization of the production system and its environment, considering ecocentric perspectives. This brings us closer to the self-decision-making capability of artificial intelligence, the practice of machine-to-machine (M2M) interaction, where human involvement in decision-making is increasingly marginalized. The IoT enables the fusion of information provided by deployed wireless sensors, data-gathering mobile robots, drones, and satellites to explore complex ecological relationships in local and global dimensions. Its significance lies, for example, in the prediction of plant protection. The paper introduces small smart data logger robots, including the Unmanned Ground Vehicles (robots) developed by the research team. These can replace sensors deployed in the Wireless Sensor Net (WSN).

The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production

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.