Search Results (734)

The integration of Internet of Things (IoT) technologies in agriculture has facilitated real-time environmental monitoring, with low-cost IoT-enabled agrometeorological stations emerging as a valuable tool for climate-smart farming. This systematic review examines low-cost IoT-based weather stations by analyzing their hardware and software components and assessing their potential in comparison to conventional weather stations. It emphasizes their contribution to improving climate resilience, facilitating data-driven decision-making, and expanding access to weather data in resource-constrained environments. The analysis revealed widespread adoption of ESP32 microcontrollers, favored for its affordability and modularity, as well as increasing use of communication protocols like LoRa and Wi-Fi due to their balance of range, power efficiency, and scalability. Sensor integration largely focused on core parameters such as air temperature, relative humidity, soil moisture, and rainfall supporting climate-smart irrigation, disease risk modeling, and microclimate management. Studies highlighted the importance of usability and adaptability through modular hardware and open-source platforms. Additionally, scalability was demonstrated through community-level and multi-station deployments. Despite their promise, challenges persist regarding sensor calibration, data interoperability, and long-term field validation. Future research should explore the integration of edge computing, adaptive analytics, and standardization protocols to further enhance the reliability and functionality of IoT-enabled agrometeorological systems. Full article

This study examines the kinematic behavior of a large-scale colluvial landslide in a coastal low-elevation forest, where rainfall, geological formations, and hydrological conditions drive substantial slope displacement. The landslide comprises a colluvial layer overlying mudstone, with downslope movement toward the coastline induced by gravitational forces and infiltration. Using GPS surveys, inclinometers, soil moisture sensors, and numerical modeling, the temporal and spatial patterns of displacement were analyzed. Maximum horizontal displacements reach 8.1 cm/year, with deep-seated movements extending over 25 m into the mudstone. Key mechanisms include weakening of the colluvium–mudstone interface and creep within saturated mudstone, while a hydraulic barrier near the coastline restricts subsurface flow. Progressive upslope migration of the freshwater-bearing mudstone zone under annual rainfall further contributes to long-term deformation. These findings provide critical insights into the hydrologically controlled kinematics of coastal colluvial landslides. Full article

Remote sensing technologies are increasingly integrated with autonomous robotic platforms to enhance data-driven decision-making in precision agriculture. Rather than replacing conventional platforms such as satellites or UAVs, autonomous ground robots complement them by enabling high-resolution, site-specific observations in real time, especially at the plant level. This review analyzes how remote sensing sensors—including multispectral, hyperspectral, LiDAR, and thermal—are deployed via robotic systems for specific agricultural tasks such as canopy mapping, weed identification, soil moisture monitoring, and precision spraying. Key benefits include higher spatial and temporal resolution, improved monitoring of under-canopy conditions, and enhanced task automation. However, the practical deployment of such systems is constrained by terrain complexity, power demands, and sensor calibration. The integration of artificial intelligence and IoT connectivity emerges as a critical enabler for responsive, scalable solutions. By focusing on how autonomous robots function as mobile sensor platforms, this article contributes to the understanding of their role within modern precision agriculture workflows. The findings support future development pathways aimed at increasing operational efficiency and sustainability across diverse crop systems. Full article

Soil moisture content is an important determinant of crop productivity, especially in agricultural systems that are dependent on rainfall. Climate variability has introduced water management challenges for smallholder farmers in Southern Africa. The emergence of unmanned aerial vehicle (UAV)-borne remote sensing offers modern solutions for monitoring soil moisture, plant health and overall crop productivity in real-time. This study evaluated the utility of UAV-acquired data in conjunction with random forest regression in predicting soil moisture content and chlorophyll across different growth stages of taro. The estimation models achieved R² values up to 0.90 with rRMSE as low as 1.25%, demonstrating the robust performance of random forest in concert with different spectral datasets in estimating soil moisture and chlorophyll. Correlation analysis confirmed the association between these two variables, with the strongest correlation observed during the vegetative stage (r = 0.81, p < 0.05) and the weakest during the late vegetative stage (r = 0.78, p < 0.05). The results showed that UAV bands were crucial in predicting soil moisture and chlorophyll across all stages. These results demonstrate the utility of remote sensing, particularly UAV-borne sensors, in monitoring crop productivity in smallholder farms. By employing UAV-borne sensors, farmers can improve on-farm water management and make better and more informed decisions. Full article

Urban gardens play a vital role in enhancing the quality of the environment and biodiversity. However, irregular rainfall and poor soil drainage due to climate change have increased the exposure of garden plants to waterlogging stress. Pseudolysimachion linariifolium (Pall. ex Link) Holub, a perennial herbaceous plant native to Northeast Asia, is widely used for its ornamental value in urban landscaping. However, its physiological responses to excess moisture conditions remain understudied. In our study, we evaluated the stress responses of P. linariifolium to waterlogging by using non-destructive analysis with drone-based multispectral imagery. We used R (ver. 4.3.2) and the Quantum Geographical Information System (QGIS ver. 3.42.1) to calculate vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Green Leaf Index (GLI), Normalized Green Red Difference Index (NGRDI), Blue Green Pigment Index (BGI), and Visible Atmospherically Resistant Index (VARI). We analyzed the indices combined with the Cumulative volumetric Soil Moisture content (SM_Cum) measured by sensors. With waterlogging treatment, NDVI decreased by 21% and GNDVI by over 34% to indicate reduced photosynthetic activity and chlorophyll content. Correlation analysis, principal component analysis, and hierarchical clustering clearly distinguished stress responses over time. Regression models using NDVI and GNDVI explained 89.7% of the variance in SM_Cum. Our results demonstrate that drone-based vegetation index analysis can effectively quantify waterlogging stress in garden plants and can contribute to improved moisture management and growth monitoring in urban gardens. Full article

We develop a year-round, field-scale framework to retrieve soil moisture and map irrigation in an arid irrigation district where crop phenology and canopy dynamics undermine static, single-season approaches. However, the currently popular TVDI application is limited during non-growing seasons. To address this gap, we introduce a season-stratified TVDI scheme—based on the LST–EVI feature space with phenology-specific dry/wet edges—coupled with a non-growing-season inversion that fuses Sentinel-1 SAR and Landsat features and compares multiple regressors (PLSR, RF, XGBoost, and CNN). The study leverages 2023–2024 multi-sensor image time series for the Yichang sub-district of the Hetao Irrigation District (China), together with in situ topsoil moisture, meteorological records, a local cropping calendar, and district statistics for validation. Methodologically, EVI is preferred over NDVI to mitigate saturation under dense canopies; season-specific edge fitting stabilizes TVDI, while cross-validated regressors yield robust soil-moisture retrievals outside the growing period, with the CNN achieving the highest accuracy (test R² ≈ 0.56–0.61), outperforming PLSR/RF/XGBoost by approximately 12–38%. The integrated mapping reveals complementary seasonal irrigation patterns: spring irrigates about 40–45% of farmland (e.g., 43.39% on 20 May 2024), summer peaks around 70% (e.g., 71.42% on 16 August 2024), and autumn stabilizes near 20–25% (e.g., 24.55% on 23 November 2024), with marked spatial contrasts between intensively irrigated southwest blocks and drier northeastern zones. We conclude that season-stratified edges and multi-source inversions together enable reproducible, year-round irrigation detection at field scale. These results provide operational evidence to refine irrigation scheduling and water allocation, and support drought-risk management and precision water governance in arid irrigation districts. Full article

In South Korea, reclaimed coastal tidelands (RTLs) are generally used for rice cultivation rather than upland cultivation; however, there is growing social pressure to change the use of RTLs to upland crop production to increase the self-sufficiency rate regarding grain. However, RTLs are not suitable for cultivating upland crops due to their high salinity, poor drainage, and shallow groundwater levels. Therefore, it is necessary to develop a cost-effective drainage method, such as surface drainage. This study investigated the effects of slope construction on surface drainage and on the growth and yield of soybean (Glycine max (L.) Merr.) in poorly drained fields at the Saemangeum RTL, which is the largest RTL district in South Korea. Slopes were constructed at angles of 0°, 3°, and 5°; soybean was sown in June 2023 (wet season) and May 2024 (dry season); and growth of soybean was monitored at the flowering, pod-filling, and harvest stages. Soil pH, electrical conductivity (EC), and mineral nitrogen (NH₄⁺ and NO₃⁻) were measured monthly, while daily changes in soil water content were measured using soil sensors. As expected, slope construction enhanced surface runoff from the upper to lower slope areas under heavy rainfall, but soil erosion was also increased. Soybean growth and yield were higher in the upper sites for the wet-season conditions mainly due to lowered moisture stress. For the dry-season, there was no significant differences in soybean growth and yield across the slopes due to drought and high temperatures during flowering and pod-filling stages. Soybean growth and yield parameters were negatively correlated with both soil water content and pH. Slope construction improves surface drainage but does not consistently translate into higher soybean yields, highlighting its limited agronomic and economic value when used alone. Instead, integrated management practices combining drainage improvement, supplemental irrigation, and soil erosion reduction need to be implemented to support sustainable upland cropping in coastal RTLs. Full article

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices. Full article

Soil moisture is one of the key hydrologic components indicating the performance of landfill final covers. Conventional compacted clay (CC) covers and evapotranspiration (ET) covers often suffer from moisture-induced stresses, such as desiccation cracking and irreversible hydraulic conductivity. Engineered turf (EnT) cover systems have been introduced recently as an alternative; however, their field-scale moisture distribution behavior remains unexplored. This study investigates and compares the soil moisture distribution characteristics of EnT, ET, and CC landfill covers at a shallow depth using one year of field-monitored data in a humid subtropical region. Three full-scale test Sections (3 m × 3 m × 1.2 m) were constructed side by side and instrumented with moisture sensors at a depth of 0.3 m. Distributional characteristics of moisture were evaluated with descriptive statistics, goodness-of-fit tests such as Shapiro–Wilk (SW) and Anderson–Darling (AD), Gaussian probability density functions, Q–Q plots, and standard-normal transformations. Results revealed that Shapiro–Wilk (W = 0.75–0.92, p < 0.001) and Anderson–Darling $(A^2=1.63\times {10}^3\mathrm{to}6.31\times {10}^3,p<0.001)$ tests rejected normality for every cover, while Levene’s test showed unequal variances between EnT and the other covers $(F>5.4\times {10}^4,$$p<0.001)$ but equivalence between CC and ET (F = 0.23, p = 0.628). EnT cover exhibited the narrowest moisture envelope $(95\%\mathrm{range}=0.156\mathrm{to}0.240{\mathrm{m}}^3/{\mathrm{m}}^3;\mathrm{CV}=10.6\%)$, whereas ET and CC covers showed markedly broader distributions (CV = 38.6 % and 33.3 %, respectively). These findings demonstrated that EnT cover maintains a more stable shallow soil moisture profile under dynamic weather conditions. Full article

Realization of the goals of precision agriculture is dependent on prescribing irrigation strategies matched to spatiotemporal variations in soil moisture on commercial farms. However, the scale at which these variations occur is not well understood. A high-spatial-density network of sensors with the ability to measure and report data over the course of a growing season is needed. In this work, design of the low-profile Smart Bluetooth Stake spatiotemporal soil moisture mapping system is presented. Smart stakes use Bluetooth Low Energy to communicate 64 MHz soil moisture impedance measurements from ground level to a receiver mounted on the center-pivot irrigation boom and equipped with a rotating high-gain parabolic antenna. Smart stakes can remain in the ground throughout the entire growing season without disrupting farm operations. A system of 86 sensors was deployed on a 50-hectare commercial field near Elberta, Utah, during the final growth stage of a crop of winter wheat. Different receiver antenna configurations were tested over the course of several weeks which included two full irrigation cycles. In the high-gain antenna configuration, data was successfully collected from 75 sensors, with successful packet transmission at ranges of approximately 600 m. Enough data was collected to construct a spatiotemporal moisture map of the field over the course of an irrigation cycle. Smart Bluetooth Stakes constitute an important advance in the spatial density achievable with direct sensors for precision agriculture. Full article

The increasing pressure on water resources caused by agricultural intensification, the rising food demand and climate change requires new irrigation strategies that improve the sustainability and efficiency of agricultural production. The objective of this study is to evaluate the performance of the digital twin (DT), Irri_DesK, in a 15-hectare commercial processing tomatoes plot in Extremadura (Spain) over two growing seasons (2023 and 2024). Three irrigation strategies were compared: conventional farmer management, management based on a remote-sensing platform (Smart4Crops) and automated scheduling using Irri_DesK DT-integrated soil moisture sensors, climate data and simulation models to adjust irrigation doses daily. Results showed that the DT-based strategy allowed for the application of regulated deficit irrigation strategies while maintaining productivity or fruit quality. In 2023, it achieved an economic water efficiency of 284.81 EUR/mm with a yield of 140 t/ha using 413 mm of water. In 2024, it maintained high production levels (126 t/ha) under more challenging conditions of spatial variability. These results support the potential of DTs for improving irrigation management in water-limited environments. Full article

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization. Full article

A landslide disaster is one of the many natural disasters that often occur in Indonesia. This disaster is one of the difficult to avoid disasters, so it often causes fatalities and large material losses. Currently, mitigation systems for landslide disasters are still less effective in their use. Early warning systems that can give information about the landslide through smartphones could be the best solution for this digital era, because society generally has smartphones and is connected through the internet. The early warning system also demanded the ability to decide the landslide status. Fuzzy logic is one of many types of artificial intelligence used as a decision support system, which is similar to human logic. Therefore, it is necessary to build an early warning system against landslides based on the Internet of Things (IoT) that can determine the status of landslides that occur based on the soil slope using the MPU6050 accelerometer sensor and moisture data using the soil moisture sensor. This system can later monitor the slope and moisture data of the soil and can transmit landslide status on smartphone applications connected to the internet. The result of this research is an IoT-based landslide early warning system that can transmit landslide and soil moisture data and transmit landslide status in the form of push notifications on smartphones using the Blynk application. Full article

The compaction quality of soil–rock mixture (SRM) subgrades critically influences infrastructure stability, but conventional settlement difference methods exhibit high spatial sampling bias (error > 15% in heterogeneous zones) and fail to characterize the overall compaction quality. These limitations lead to under-compaction (porosity > 25%) or over-compaction (aggregate fragmentation rate > 40%), highlighting the need for real-time monitoring. This study develops an intelligent compaction system integrating (1) vibration acceleration sensors (PCB 356A16, ±50 g range) for compaction meter value (CMV) acquisition; (2) near-infrared (NIR) moisture meters (NDC CM710E, 1300–2500 nm wavelength) for real-time moisture monitoring (sampling rate 10 Hz); and (3) an embedded edge-computing module (NVIDIA Jetson Nano) for Python-based data fusion (FFT harmonic analysis + moisture correction) with 50 ms processing latency. Field validation on Linlin Expressway shows that the system meets JTG 3430-2020 standards, with the compaction qualification rate reaching 98% (vs. 82% for conventional methods) and 97.6% anomaly detection accuracy. This is the first system integrating NIR moisture correction (R² = 0.96 vs. oven-drying) with CMV harmonic analysis, reducing measurement error by 40% compared to conventional ICT (Bomag ECO Plus). It provides a digital solution for SRM subgrade quality control, enhancing construction efficiency and durability. Full article

Optimal application of nitrogen fertilizer is critical for soil characteristics and soil health. This study examined the effects of three rates of nitrogen fertilizer applications, which are lower rate (Treatment 1 (T1)-241 kg/ha), recommended rate (Treatment 2 (T2)-269 kg/ha), and higher rate (Treatment 3 (T3)-297 kg/ha), and their impacts on soil temperature, soil moisture and soil electrical conductivity at two different depths (0–30 cm and 30–60 cm) in maize cultivation at the Prairie View A & M university research farm in Texas. Soil moisture, soil temperature, and electrical conductivity (EC) sensors were installed in 27 plots to collect these data. Results showed that EC is lower at surface depth with all fertilizer application rates than at root zone soil depths. In the meantime, EC is increasing in the root zone soil depth with the increase in fertilizer rate. This study indicated that the moderate application (269 kg/ha, T2) which is also recommended rate, showed better soil health parameters and efficiency in comparison to other application rates maintaining stable and moderate electrical conductivity values (0.2 mS/cm at depth 2) and the highest median moisture content at the significant root zone depth (about 0.135 m³/m³), reducing nutrient leaching and salt accumulation. Also, a humid, warm climate in southern Texas specifically affects increasing nitrogen losses via leaching, denitrification, and volatilization compared to cooler regions, which requires higher application rates. Plant growth and yield results further confirmed that the recommended rate achieved the greatest plant height (157.48 cm) compared to T1 (153.07 cm). Ear diameters were also higher at the recommended rate, reaching 4.65 cm ears than in Treatment 3. However, grain productivity was highest under the lower fertilizer rate T1, with wet and dry yields of 11,567 kg/ha and 5959 kg/ha, respectively, compared to 10,033 kg/ha (wet) and 5047 kg/ha (dry) at T2, and 7446 kg/ha (wet) and 4304 kg/ha (dry) at T3. These findings suggest that while the moderate fertilizer rate (269 kg/ha) enhances soil health and crop growth consistency, the lower rate (241 kg/ha) can maximize productivity under the humid, warm conditions of southern Texas. This research highlights the need for precise nitrogen management strategies that balance soil health with crop yield. Full article