Search Results (314)

Pasture quality monitoring is essential for optimizing grazing management and reducing the incidence of nutritional and metabolic disorders in ruminants, yet conventional field-based measurements remain labor-intensive and limited in spatial coverage. This review examines how remote sensing (RS) technologies can support pasture-based livestock systems by providing timely, scalable assessments of biomass, botanical composition, and nutritive attributes. Data from multispectral, hyperspectral, radio detection and ranging (RADAR), and light detection and ranging (LiDAR) sensors, acquired via satellite, unmanned aerial vehicle (UAV), and proximal platforms, are combined with machine learning (ML) methods and radiative transfer models to derive pasture biophysical and quality indicators. The reviewed evidence shows that RS reliably estimates pasture biomass and structural traits, while advances in spectral unmixing, data fusion, and artificial intelligence (AI) improve the characterization of heterogeneous swards and support emerging indicators related to forage quality. Integrating these remotely sensed metrics into grassland decision-support frameworks can enhance grazing allocation, inform fertilization and irrigation decisions, and help detect conditions associated with nutritional imbalances. Overall, the synthesis demonstrates that RS, particularly when combined with advanced modelling and cloud-based processing, offers a robust pathway for improving pasture monitoring and strengthening the nutritional management of ruminants, thereby supporting more sustainable and animal welfare-focused grazing systems. Full article

Agriculture is increasingly challenged by water scarcity, climate change, and rising energy demands, requiring more efficient and sustainable irrigation solutions. Conventional irrigation systems often lack the capability to adapt their operation to changing soil conditions and renewable energy availability, resulting in inefficient water and energy use. This study proposes a digital twin-based framework for energy management and irrigation optimization in photovoltaic (PV)-powered smart agriculture systems using Internet of Things (IoT) soil monitoring. The proposed system integrates a physical irrigation infrastructure, an IoT monitoring network, a fuzzy logic control layer, and a digital twin environment that periodically synchronizes the virtual model with IoT measurements to support the system representation and decision-making. The digital twin models soil moisture, temperature, nutrient levels, PV energy generation, battery state of charge, and irrigation water consumption. The virtual representation was periodically aligned with the physical system using measurements transmitted through the long-range (LoRa)-based network. An energy-aware irrigation scheduling strategy was developed to optimize irrigation timing based on soil conditions, battery status, and solar energy availability. The framework was evaluated using field data collected in a real apple orchard through an ESP32-based IoT platform and a standalone PV-powered irrigation system; quantitative experimental validation was performed for the soil twin. The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R² of 0.981, based on experimental field measurements. The energy twin and irrigation twin were evaluated using experimentally acquired sensor data together with model-based performance assessment, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture. Full article

Freshwater scarcity is an increasing concern for agricultural systems, making water footprint (WF) assessment an important tool for evaluating water use in crop production and supporting sustainable water resource management. Two main methodological approaches are widely used: the water footprint network (WFN) framework and Life Cycle Assessment (LCA)-based approaches. This review synthesizes 27 peer-reviewed studies that use tomato production as a representative case study to compare the two approaches across methodological frameworks, geographical contexts, production systems, system boundaries, functional units, and research objectives. The reviewed studies revealed substantial methodological heterogeneity, with WFN representing the predominant approach, whereas LCA-based applications remained comparatively limited. WFN was primarily applied to quantify water use and identify water-use hotspots, whereas LCA-based approaches focused on evaluating the potential environmental impacts of water consumption under local water-scarcity conditions. The review also highlights the ongoing debate surrounding scarcity-weighted indicators and the interpretation of water footprint results. Reported water footprint values varied considerably due to differences in climate, irrigation management, crop productivity, system boundaries, and methodological assumptions, particularly in the calculation of gray water footprint. Overall, the findings indicate that WFN and LCA-based approaches provide complementary perspectives on freshwater sustainability. Their combined application can support more comprehensive water assessments and better-informed decision-making in agricultural systems, while greater methodological harmonization is needed to improve the comparability of future studies. Full article

Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (η_p² = 0.63). CIELUV v* explained the greatest model variation (R²m = 0.94) and responded most to fertilizer (η_p² = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems. Full article

Efficient irrigation management requires complementary information on atmospheric demand, soil conditions, and crop water stress. This study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within a unified monitoring and control framework. The system combines LoRa communication, ESP32-based sensor nodes, soil and meteorological sensing, FAO-56 reference evapotranspiration ($E{T}_0$), and canopy-temperature-based Crop Water Stress Index (CWSI). Irrigation decisions rely on the complementary use of $E{T}_0$, in situ soil measurements, and CWSI rather than on a single indicator. A hybrid time-, event-, and query-driven acquisition strategy was implemented to adapt node activity and limit communication overhead. The system was deployed under outdoor conditions in Oujda, Morocco, demonstrating integrated sensing, wireless data transmission, crop-stress monitoring, and automated irrigation control. Energy characterization further showed distinct consumption profiles across sensing, communication, actuation, and low-power operating states, supporting the use of duty cycling to limit active node operation. The results demonstrate the feasibility of integrating environmental, soil, and crop-level information within a low-power IoT framework for adaptive irrigation management. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

The integration of sensing systems, Artificial Intelligence (AI), and data analytics technologies is enabling the development of digital twins for monitoring and managing complex built environments. University campuses represent suitable scenarios for the deployment and evaluation of these technologies due to their diversity of facilities, heterogeneous operational systems, and dynamic usage patterns. This paper presents the design and deployment of a campus-scale operational digital twin developed for the University of Alicante Smart Campus. The proposed environment integrates multiple data sources including indoor environmental sensors, electricity and water consumption monitoring systems, photovoltaic generation data, irrigation networks, and WiFi connectivity information used as a proxy for occupancy estimation. Through a continuously updated and spatially synchronized digital representation of the campus, the platform supports environmental comfort monitoring, occupancy analysis, environmental quality assessment, anomaly detection, alert generation, and AI-enabled analytical services. A distinguishing characteristic of the proposed approach is its long-term real-world deployment. The system currently manages several years of historical data comprising hundreds of millions of time-series measurements collected from distributed monitoring systems across the campus. The results demonstrate the feasibility of maintaining a campus-scale digital twin operating as an institutional monitoring and decision-support environment. Full article

Decision support systems (DSSs) help farmers and decision-makers to find cropping systems which increase productivity while decreasing the use of fertilizers and irrigation; however, few DSSs exist which integrate soil carbon models, especially in Mediterranean climate. Here, we test whether RothC20_N, a version of the widely used RothC model adapted for Mediterranean and arid climates, can estimate the soil water content (SWC), soil organic carbon (SOC), and soil respiration (Rs), observed in two different cropping systems, one traditional and the other informed by the DSS by Horta Srl. The model was calibrated and tested against two long-term (8 years) field experiments in two different areas in Italy. The two sites showed characteristically dry soils during the summer period; RothC20_N was able to predict correctly the soil water content time series observed in both sites. RothC20_N could predict the measured SOC and heterotrophic respiration time series with Nash–Sutcliff efficiency ~0.3, normalized root mean squared error ~0.4, and Kling–Gupta efficiency > 0. This study shows that the multi-objective calibrated RothC20_N is an interesting candidate for inclusion in a DSS to broaden the potential of the DSS to increase the sustainability of agricultural practices by also addressing soil carbon dynamics while maintaining high agricultural productivity. Full article

Under the dual pressures of freshwater scarcity and intensifying soil salinization in arid regions, the efficient use of brackish water has become a critical pathway for alleviating regional water stress. This narrative review seeks to synthesize a new paradigm characterized by deep integration of smart agriculture with brackish water irrigation. Further review shows that smart agriculture can open a new pathway for precision regulation of brackish water irrigation. Through reviewing existing studies on how brackish water irrigation affects soil properties and crop growth, we summarized the issues that emerged and proposed an integrated framework for sustainable brackish water application combined with smart agricultural management. Conventional brackish water irrigation increases the risks of deterioration in soil physicochemical properties, disruption of microbial community structure, and inhibition of crop growth and yield. On this basis, we propose a paradigm framework for smart brackish water irrigation, consisting of paradigm foundations, key technologies, application scenarios, and long-term goals. This framework clarifies the core connotations of intelligent water-salt coordination, dynamic threshold management, and multi-source data-driven decision-making, and promotes the transition of brackish water irrigation toward greater precision, intelligence, and system integration. This review can establish a technical system for smart brackish water utilization and provide both theoretical and practical support for the high-quality, efficient, and sustainable use of brackish water resources in arid regions. Full article

Soil monitoring is a key component of precision agriculture and environmental sensing systems, where reliable measurements support irrigation management and crop monitoring. Although high-end sensing platforms provide accurate measurements, their cost limits widespread adoption, particularly in resource-constrained agricultural environments. Low-cost soil sensors, such as widely available 7-in-1 probes capable of measuring soil moisture, temperature, electrical conductivity, and pH, offer a scalable alternative for distributed monitoring; however, their limited accuracy raises concerns regarding their reliability for decision-support systems. This study investigates whether measurements from a single low-cost 7-in-1 soil sensor contain sufficient information to reconstruct the outputs of a commercial high-end sensing platform (CropX), specifically volumetric water content (VWC) and pore-water electrical conductivity (ECpw). Field data were collected in a tropical Hass avocado orchard in Colombia, and four machine learning models were evaluated to reconstruct CropX measurements from low-cost sensor signals at three soil depths (20, 41, and 66 cm). Random Forest achieved the highest reconstruction performance, with coefficient of determination R2 values between 0.9965 and 0.9986 and consistently low root mean square error (RMSE) and mean absolute error (MAE) across depths. Out-of-bag validation and multi-seed stability analyses confirmed the robustness of the models despite the limited dataset size. A chronological validation (80–20%) showed substantially reduced performance, indicating that the proposed approach is more suitable for reconstructing high-end sensor signals under concurrent measurement conditions than for strict temporal extrapolation. Therefore, the framework should be interpreted as a virtual sensing strategy for reconstructing simultaneous CropX measurements from low-cost sensor observations rather than as a standalone model for predicting future soil conditions without periodic recalibration. These results demonstrate that low-cost multi-parameter sensors can support high-fidelity virtual reconstruction of high-end soil measurements, contributing to the development of scalable and cost-effective soil monitoring systems for precision agriculture. Full article

Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of $N=150$ observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved $R^2=0.842$ and $RMSE=0.0705$ on a normalized target scale, corresponding to a $7.05\%$ error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached $91.1\%$ accuracy (Cohen’s Kappa $=0.91$). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline ($\mathrm{\Delta }{R}^2=+0.052$) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions. Full article

Spatially explicit crop yield information is needed for regional environmental modeling, sustainability assessment, and agricultural decision support, yet official yield statistics are commonly reported only at aggregated administrative scales. This study introduces the NAYD R package, a reproducible geospatial workflow for converting county-level harvested area and yield statistics into spatially explicit production units and zonal clusters while preserving consistency with official records. County-level statistics from the USDA National Agricultural Statistics Service were integrated with USDA Cropland Data Layer crop masks, multi-sensor NDVI products, and satellite-derived evapotranspiration from OpenET SSEBop. An NDVI-based eligibility filter refined crop masks toward reported harvested area, while normalized NDVI and evapotranspiration layers were combined into spatial weighting surfaces and aggregated into contiguous production units and graph-based clusters. Case studies for cotton and winter wheat in Texas showed that the eligibility filter removed approximately 20–40% of CDL-classified pixels while maintaining consistency with reported harvested area and preserving plausible spatial gradients associated with irrigated and dryland systems. Evaluation against independent Texas A&M AgriLife variety trial data indicated that the disaggregated clusters reproduced plausible spatial patterns of yield variability while retaining the county-level NASS constraints. The workflow provides an open-source framework for generating statistically consistent production zones for regional crop modeling, environmental assessment, and sustainable agricultural planning. Full article

Cloud-dependent irrigation platforms can support remote monitoring, but their use in precision fertigation is limited when local decisions must be made quickly and reliably. Network delay, temporary disconnection, and the use of single-point measurements may all reduce the ability of a system to respond to spatial variation in soil moisture and nutrient demand. In this work, an edge-controlled precision fertigation system was developed by combining multi-parameter soil sensing, spatial task scheduling, and a 6-DOF robotic manipulator. The ESP32 controller runs a preemptive FreeRTOS scheduler, allowing sensor acquisition, inverse-kinematics calculation, and pump actuation to be handled as separate tasks. A Kalman filter was used to smooth soil moisture measurements, and a hysteresis-based control strategy was adopted to reduce false triggering and repeated pump switching. To improve fertigation safety, a hardware–software interlock was added so that fertilizer delivery is always accompanied by water delivery. Hardware-in-the-Loop simulation and a 14-day field deployment were used to evaluate the system. The controller achieved an end-to-end latency of less than 38 ms and maintained operation during network interruptions through cached local parameters. After calibration, the robotic end-effector positioning error was reduced to ±2.4 mm. The hysteresis strategy lowered daily pump cycling by 71%. Based on prototype duty-cycle data and seasonal extrapolation, the projected seasonal water use and fertilizer demand were 44% and 38% lower, respectively, than those estimated for a uniform application. These values should be interpreted as model-based projections rather than direct season-long measurements. During 72 h of continuous operation, no Modbus faults were observed, and RTOS heap fragmentation remained stable. Overall, the results suggest that edge-based deterministic control can provide a practical route for precision fertigation where both spatial variability and intermittent connectivity must be considered. Full article

Artificial Intelligence (AI) has become a key enabler in soil science and agriculture, supporting advanced modeling, monitoring, and decision-making processes. This systematic review synthesizes recent developments in AI-based soil characterization and agricultural applications, with emphasis on soil physicochemical properties, digital soil mapping, irrigation management, and crop yield prediction. Following the PRISMA 2020 framework, a structured search of the Scopus database identified 196 eligible studies published between 2018 and 2026. The reviewed literature reveals a clear transition toward data-driven approaches, with machine learning and deep learning models dominating recent research. Random Forest, Support Vector Machines, gradient boosting methods, artificial neural networks, Convolutional Neural Networks, and Long Short-Term Memory architectures were the most frequently reported techniques. The primary data sources included in situ sensors, laboratory measurements, remote sensing imagery, and environmental covariates, often integrated through multi-source data fusion frameworks. The results indicate that tree-based ensemble models provide robust performance across diverse soil properties, whereas deep learning models are particularly effective for spatiotemporal prediction and remote sensing applications. AI-driven systems are increasingly used to support precision agriculture through irrigation optimization, crop yield forecasting, digital soil mapping, and soil health monitoring. However, challenges remain regarding data quality and availability, model transferability across regions, and the limited interpretability of complex models. The findings highlight current research trends, methodological challenges, and future opportunities for the development of reliable and scalable AI-driven soil and agricultural systems. Full article

Climate change is increasing the frequency of droughts, raising the need for sustainable irrigation management in viticulture. This study evaluated the effects of three deficit irrigation regimes—well-watered (WW), mild deficit (MiD), and moderate deficit (MoD)—implemented through the decision support system Vintel^® in a Pinot gris vineyard in Friuli Venezia Giulia (Italy). Vine physiology, grape ripening, and wine aroma profile were assessed across two seasons. The water deficit treatments modulated yield parameters (specifically, cluster weight was reduced by 12% and 10% for Mid and Mod as compared to WW) and delayed sugar accumulation, particularly under MoD (9% Brix reduction as compared to WW). While basic wine composition largely reflected grape maturity, volatile aroma compounds showed variable responses to irrigation and were strongly modulated by seasonal conditions. MiD had a minimal impact on the aroma profile, whereas MoD led to reduced sugar and altered volatile composition, especially under hot and dry conditions. DSS-based mild-deficit irrigation can be adopted to reduce vineyard water consumption without compromising Pinot gris wine quality. Full article