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

Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species.

To better understand the potential of soils, understanding how soil properties vary over time and in-field is essential to optimize the cultivation and site-specific technologies in crop production. This article aimed at determining the within-field mapping of soil chemical and physical properties, vegetation index, and yield of maize in 2002, 2006, 2010, 2013, and 2017, respectively. The objectives of this five-year field study were: (i) to assess the spatial and temporal variability of attributes related to the maize yield; and (ii) to analyse the temporal stability of management zones. The experiment was carried out in a 15.3 ha research field in Hungary. The soil measurements included sand, silt, clay content (%), pH, phosphorous (P₂O₅), potassium (K₂O), and zinc (Zn) in the topsoil (30 cm). The apparent soil electrical conductivity was measured in two layers (0–30 cm and 30–90 cm, mS/m) in 2010, in 2013, and in 2017. The soil properties and maize yields were evaluated in 62 management zones, covering the whole research area. The properties were characterized as the spatial-temporal variability of these parameters and crop yields. Classic statistics and geostatistics were used to analyze the results. The maize yields were significantly positively correlated (r = 0.62–0.73) with the apparent electrical conductivity (Veris_N3, Veris_N4) in 2013 and 2017, and with clay content (r = 0.56–0.81) in 2002, 2013, and 2017.

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.

Background: Accurate prediction of soil moisture content (SMC) is crucial for sustainable irrigation management and enhancing resilience against climate change. However, in-situ sensing offers accurate point-scale measurements but lacks spatial representativeness, while satellite-based offer spatial coverage but are either too coarse at field scale or indirect and cloud -sensitive. Integrating satellite observation with ground-based monitoring and IoT meteorological data could exploit complementary strengths by linking canopy conditions and atmospheric drivers to reliable in-field reference measurements. Method: This study predicts SMC at five depths (5 to 80 cm) for two soil texture classes (loam and silt loam) using Extreme Gradient Boosting (XGBoost) by integrating Sentinel-2 vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) with Internet of Things (IoT)-derived meteorological data using two input scenarios and gravimetric SMC as reference for model training and evaluation. Results: The model trained with combined inputs achieved higher accuracy compared with using only vegetation indices as predictors in both soil textures and all depths. This was especially evident in loam soil at 5 and 20 cm depth, with R² values of 0.95 and 0.79 and RMSE values of 0.88% and 1.46%, respectively, compared to R² values of 0.70 and 0.69 and RMSE values of 2.26% and 1.77% when using vegetation indices only. The model achieved near-perfect accuracy in silt loam with R² = 0.99 at 5–20 cm and RMSE = 0.49–0.40% at same depths. SHapley Additive exPlanations (SHAP) analysis identified NDVI as the most influential predictor in surface soil layers (mean SHAP = 0.11–0.22), reflecting its strong sensitivity to canopy vigor. In contrast, solar radiation emerged as key determinant in deeper soil layers (60 and 80 cm; SHAP = 0.12–0.18), highlighting the importance of atmospheric evaporative demand in controlling subsoil moisture dynamics. Conclusions: The model’s accuracy and interpretability enable depth-specific decision support for irrigation timing and water use efficiency under variable weather conditions, while providing actionable driver insights for climate-adaptive management aligned with SDGs 6 and 13. The approach is validated for loam and silt loam textures using optical Sentinel-2 indices, which are subject to cloud cover and revisit latency; therefore, the current framework is not suitable for real-time irrigation scheduling without accounting for these delays. Future integration with SAR and gap-filling strategies would be required for operational real-time applications. Graphical Workflow for depth-specific soil moisture prediction using Sentinel-2 vegetation indices and IoT meteorological data with explainable XGBoost (SHAP).