Search Results (1,053)

Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation. Full article

The intensification of agricultural land use (A-LUI) is a central driver of global environmental change, affecting soil health, water quality, biodiversity, and greenhouse gas balances. Monitoring A-LUI remains challenging because it is shaped by multiple management practices, ecological processes, and spatio-temporal dynamics. This review provides a comprehensive synthesis of existing definitions and standards of A-LUI at national and international levels (FAO, OECD, World Bank, EUROSTAT) and evaluates in situ methods alongside the rapidly expanding potential of remote sensing (RS). We introduce a novel RS-based taxonomy of A-LUI indicators, structured into five complementary categories: trait, genesis, structural, taxonomic, and functional indicators. Numerous examples illustrate how traits and management practices can be translated into RS proxies and linked to intensity signals, while highlighting key challenges such as sensor limitations, cultivar variability, and confounding environmental factors. We further propose an integrative framework that connects management practices, plant and soil traits, RS observables, validation needs, and policy relevance. Emerging technologies—such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration—are discussed as promising pathways to advance the monitoring of A-LUI across scales. By compiling and structuring RS-derived indicators, this review establishes a conceptual and methodological foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, thereby supporting both scientific progress and evidence-based agricultural policy. Full article

Climate change poses significant challenges to viticulture worldwide, with Eastern European vineyards experiencing increased water stress due to rising temperatures, irregular precipitation patterns, and prolonged drought periods. These climatic shifts hurt vine phenology, grape quality, and overall productivity. In response, adaptive irrigation strategies such as Regulated Deficit Irrigation (RDI) have gained attention for optimizing water use while preserving grape quality. Concurrently, the adoption of smart agriculture technologies—including soil moisture sensors, automated weather stations, remote sensing, and data-driven decision support systems—enables precise monitoring and real-time management of vineyard water status. This review synthesizes recent studies from Eastern Europe, emphasizing the necessity of integrating climate adaptation measures with intelligent irrigation management to enhance vineyard resilience and sustainability under increasing climate variability. Full article

The growing threat of organic pollution to surface waters necessitates the development of rapid and scalable monitoring tools that transcend the limitations of the standard 5-day biochemical oxygen demand (BOD₅) test. This study presents a novel approach by developing a highly stable and rapid BOD biosensor based on the microorganism Paracoccus yeei, immobilized within a sol–gel-derived xerogel matrix synthesized on a polyhydroxybutyrate (PHB) substrate. The PHB-supported xerogel significantly enhanced microbial viability and sensor stability. This biosensor demonstrated a correlation (R² = 0.93) with the standard BOD₅ method across 53 diverse water samples from the Tula region, Russia, providing precise results in just 5 min. The second pillar of our methodology involved analyzing multi-year Landsat satellite imagery via the Global Surface Water Explorer to map hydrological changes and identify zones of potential anthropogenic impact. The synergy of rapid ground-truth biosensor measurements and remote sensing analysis enabled a comprehensive spatial assessment of water quality, successfully identifying and ranking pollution sources, with wastewater discharges and agro-industrial facilities constituting the most significant factors. This work underscores the high potential of PHB–xerogel composites as efficient immobilization matrices and establishes a powerful, scalable framework for regional environmental monitoring by integrating advanced biosensor technology with satellite observation. Full article

Environmental pollution is a global issue presenting risks to ecosystems and human health through release of toxic gases, existence of volatile organic compounds (VOCs) in the environment, and heavy metal contamination of waters and soils. To effectively address this issue, reliable and real-time monitoring technology is imperative. Metal–organic frameworks (MOFs) are a disruptive set of materials with high surface area, tunable porosity, and abundant chemistry to design extremely sensitive and selective pollutant detection. This review article gives an account of recent advances towards sensor technology for MOFs with application specificity towards gas and environment monitoring. We critically examine optical, electrochemical, and resistive platforms and their interfacing with embedded electronics and edge artificial intelligence (edge-AI) to realize smart, compact, and energy-efficient monitoring tools. We also detail critical challenges such as scalability, reproducibility, long-term stability, and secure data management and underscore transforming MOF-based sensors from lab prototype to functional instruments to ensure safe coverage of human health and to bring about sustainable environmental management. Full article

Processing complex ore remains a challenge due to energy-intensive grinding and complex beneficiation and pyrometallurgical treatments that consume large amounts of water whilst generating significant waste and polluting the environment. Sensor-based ore sorting, which separates ore particles based on their physical or chemical properties before downstream processing, is emerging as a transformative technology in mineral processing. However, its application to complex and heterogeneous ores remain limited by the constraints of single-sensor systems. In addition, existing hybrid sensor strategies are fragmented and a consolidated framework for implementation is lacking. This review explores these challenges and underscores the potential of multimodal sensor integration for complex ore pre-concentration. A multi-sensor framework integrating machine learning and computer vision is proposed to overcome limitations in handling complex ores and enhance sorting efficiency. This approach can improve recovery rates, reduce energy and water consumption, and optimize process performance, thereby supporting more sustainable mining practices that contribute to the United Nations Sustainable Development Goals (UNSDGs). This work provides a roadmap for advancing efficient, resilient, and next-generation mineral processing operations. Full article

Advances in Earth observation technology using multispectral imagery from satellite Earth observation systems and sensors mounted on unmanned aerial vehicles (UAVs) are enabling more accurate crop monitoring. These images, once processed, facilitate the analysis of crop health by enabling the study of crop vigour, the calculation of biomass indices, and the continuous temporal monitoring using vegetation indices (VIs). These indicators allow for the identification of diseases, pests, or water stress, among others. This study compares images acquired with the Altum PT sensor (UAV) and Super Dove (satellite) to evaluate their ability to detect specific problems in super-intensive olive groves at two critical times: January, during pruning, and April, at the beginning of fruit development. Four different VIs were used, and multispectral maps were generated for each: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), the Normalized Difference Red Edge Index (NDRE) and the Leaf Chlorophyll Index (LCI). Data for each plant (n = 11,104) were obtained for analysis across all dates and sensors. A combined methodology (Spearman’s correlation coefficient, Student’s t-test and decision trees) was used to validate the behaviour of the variables and propose predictive models. The results showed significant differences between the sensors, with a common trend in spatial patterns and a correlation range between 0.45 and 0.68. Integrating both technologies enables multiscale assessment, optimizing agronomic management and supporting more sustainable precision agriculture. Full article

Water-quality monitoring plays a vital role in protecting and managing water resources, maintaining ecological balance and safeguarding human health. At present, the traditional monitoring technology is associated with risks of low sampling efficiency, long response time, high economic cost and secondary pollution of water samples, and cannot guarantee the accuracy and real-time determination of monitoring data. Remote sensing (RS) technology and sensors are used to automatically realize the real-time monitoring of water quality. In this paper, the principles and composition of remote monitoring systems are systematically summarized. For the RS technology, indicators including chlorophyll-a, turbidity and total suspended matter/solids, colored dissolved organic matter, electrical conductivity (EC), dissolved oxygen (DO), temperature and pH value were considered, and for sensors monitoring, the parameters of pH value, temperature, oxidation reduction potential, DO, turbidity, EC and salinity, and total dissolved solids were analyzed. The practical applications of remote monitoring in surface water, marine water and wastewater are introduced in this context. In addition, the advantages and disadvantages of remote monitoring systems are evaluated, which provides some basis for the selection of remote monitoring systems in the future. Full article

The topic proposal concerns the axes of climate operation and modification, the consequences and/or benefits of the flow of the economy, as well as the risks to social security, amidst the evolution of human interventions, which the Anthropocene highlights. Atmospheric data demonstrates the interaction of gaseous pollutants and aerosols, with the contribution of different emission and pollution sources to its chemical composition. At the same time, satellite remote sensing of precipitation and the water cycle reveal an imbalance in components and effects, in an environment of rapid rates of commercial production and human mobility in the developed world. How does mobility prevent the full observation and modeling of the elements involved (in atmospheric and hydrological data)? What is the role of multi-sensor technologies for detecting gases and what are their applications in decontamination? With sources from bibliographic reviews, data were collected from the detection of point sources of gases and dynamic analyses of the extent of the water surface, in order to highlight the descriptive characteristics of the meteorological phenomena and their activity. The scientific approach to analyzing the individual data is based on the techno-scientific Actor-Network Theory, in order to test their connection and contribution to the overall problematic result. The aim of this study is to build an interdisciplinary analysis with documentation of vulnerabilities in the expression of weather phenomena, of the present geological time. The ambition of the study is to propose principles of regulation and precaution, related to the sustainable development of geo-resources and ways to reduce vulnerability. Full article

Curing plays a fundamental role in determining the mechanical performance, durability, and sustainability of concrete structures. Traditional curing practices, such as water and air curing, are widely used but often limited by long durations, high water demand, and reduced effectiveness under extreme climatic conditions. In response, advanced curing methods such as steam, microwave, electric, autoclave, and accelerated carbonation have been developed to accelerate hydration, refine pore structures, and enhance durability. This review critically examines the performance of both conventional and advanced curing strategies across a range of concrete systems. Findings show that microwave curing achieves up to 85–95% of 28-day wet-cured strength within 24 h, whilst autoclave curing enhances early strength by 40–60%. Electric curing reduces energy demand by approximately 40% compared to steam curing, and carbonation curing lowers carbon dioxide emissions by 30–50% through carbon sequestration. While steam and autoclave curing provide rapid early strength, they may compromise long-term durability through microcracking and increased porosity. No single method was identified as universally optimal; the effectiveness depends on the mix design, application, and environmental conditions. The review highlights future opportunities in smart curing systems, integrating Internet of Things (IoT), sensor technologies, and AI-driven predictive control to enable real-time optimisation of curing conditions. Such innovations represent a critical pathway for improving concrete performance while addressing sustainability targets in the building and construction industry. Full article

This paper introduces an innovative open-source hardware platform designed for multi-sensor environmental monitoring, rooted in the outcomes of the “Smart Water” project. The primary objective of this platform is to facilitate advanced PCB design education by offering students a modular, expandable, and feature-rich embedded hardware environment. The platform serves as a practical training ground, enabling students to experiment with diverse sensing techniques and refine their skills in the intricacies of PCB design. The “Smart Water” project, which forms the foundation of this educational platform, has yielded invaluable insights into environmental monitoring technologies. Leveraging these findings, our hardware platform integrates a variety of sensors capable of measuring crucial environmental parameters such as water quality, temperature, and atmospheric conditions. The modular design allows students to explore various sensor combinations and experiment with custom configurations, fostering a deeper understanding of sensor integration and optimization. Key features of the platform include its expandability, encouraging students to develop add-on modules for specific applications or to enhance existing functionalities. This approach not only promotes creativity but also instills a sense of ownership and collaboration among students, as they contribute to the continual evolution of the hardware platform. The feature-rich nature of the embedded system enables comprehensive experimentation in sensor data acquisition, processing, and communication, providing a holistic learning experience. Full article

In Northeast Brazil, climatic factors and technology synergistically enhance melon productivity and fruit quality. However, the region requires further research on the efficient use of water resources, particularly in determining the crop coefficient (Kc), which comprises the evaporation coefficient (Ke) and the transpiration coefficient (Kcb). Air temperature affects crop growth and development, altering the spectral response and the Kcb. However, the direct influence of air temperature on Kcb and spectral response remains underemphasized. This study employed unmanned aerial vehicle (UAV) with RGB and Red-Green-NIR sensors imagery to extract biophysical parameters for improved water management in melon cultivation in semiarid northern Bahia. Field experiments were conducted during two distinct periods: warm (October–December 2019) and cool (June–August 2020). The ‘Gladial’ and ‘Cantaloupe’ cultivars exhibited higher Kcb values during the warm season (2.753–3.450 and 3.087–3.856, respectively) and lower during the cool season (0.815–0.993 and 1.118–1.317). NDVI-based estimates of Kcb showed strong correlations with field data (r > 0.80), confirming its predictive potential. The results demonstrate that UAV-derived NDVI enables reliable estimation of melon Kcb across seasons, supporting its application for evapotranspiration modeling and precision irrigation in the Brazilian semiarid context. Full article

The optical microscope is an indispensable observation instrument that has fundamentally contributed to progress in science and technology. Dark-field microscopy and scattered light imaging techniques enable high-contrast observation of nanoparticles in water. However, the scattered light is focused by the optical lenses, resulting in a blurred image of the nanoparticle structure. Here, we developed a projection optical microscope (PROM), which directly observes the scattered light from the nanoparticles without optical lenses. In this method, the sample is placed below the focus position of the microscope’s objective lens and the projected light is detected by an image sensor. This enables direct observation of the sample with a spatial resolution of approximately 20 nm. Using this method, changes in the aggregation state of nanoparticles in solution can be observed at a speed faster than the video frame rate. Moreover, the mechanism of such high-resolution observation may be related to the quantum properties of light, making it an interesting phenomenon from the perspective of optical engineering. We expect this method to be applicable to the observation and analysis of samples in materials science, biology and applied physics, and thus to contribute to a wide range of scientific, technological and industrial fields. Full article

Population growth drives increasing energy demands, agricultural production, and organic waste generation. The organic waste contributes to greenhouse gas emissions and increasing landfill burdens, highlighting the need for novel closed-loop technologies that integrate water, energy, and food resources. Within the context of the Water–energy–food Nexus (WEF), wastewater can be recycled for food production and food waste can be converted into clean energy, both contributing to environmental impact reduction and resource sustainability. A novel household-scale, closed-loop WEF system was designed, installed and operated to manage organic waste while retrieving water for irrigation, nutrients for plant growth, and biogas for energy generation. The system included a biodigester for energy production, a sand filter system to regulate nutrient levels in the effluent, and a hydroponic setup for growing food crops using the nutrient-rich effluent. These components are operated with a daily batch feeder coupled with automated sensors to monitor effluent flow from the biodigester, sand filter system, and the feeder to the hydroponic system. This novel system was operated continuously for two months using typical household waste composition. Controlled experimental tests were conducted weekly to measure the nutrient content of the effluent at four locations and to analyze the composition of biogas. Gas chromatography was used to analyze biogas composition, while test strips and In-Situ Aqua Troll Multi-Parameter Water Quality Sonde were employed for water quality measurements during the experimental study. Experimental results showed that the system consistently produced biogas with 76.7% (±5.2%) methane, while effluent analysis confirmed its potential as a nutrient source with average concentrations of phosphate (20 mg/L), nitrate (26 mg/L), and nitrite (5 mg/L). These nutrient values indicate suitability for hydroponic crop growth and reduced reliance on synthetic fertilizers. This novel system represents a significant step toward integrating waste management, energy production, and food cultivation at the source, in this case, the household. Full article

Accurate mapping of small water structures and streambeds is essential for hydrological modeling, erosion control, and landscape management. While traditional geodetic methods such as GNSS and total stations provide high precision, they are time-consuming and require specialized equipment. Recent advances in mobile technology, particularly smartphones equipped with LiDAR sensors, offer a potential alternative for rapid and cost-effective field data collection. This study assesses the accuracy of the iPhone 14 Pro’s built-in LiDAR sensor for mapping streambeds and retention structures in challenging terrain. The test site was the Dílský stream in the Oslavany cadastral area, characterized by steep slopes, rocky surfaces, and dense vegetation. The stream channel and water structures were first surveyed using GNSS and a total station and subsequently re-measured with the iPhone. Several scanning workflows were tested to evaluate field applicability. Results show that the iPhone LiDAR sensor can capture landscape features with useful accuracy when supported by reference points spaced every 20 m, achieving a vertical RMSE of 0.16 m. Retention structures were mapped with an average positional error of 7%, with deviations of up to 0.20 m in complex or vegetated areas. The findings highlight the potential of smartphone LiDAR for rapid, small-scale mapping, while acknowledging its limitations in rugged environments. Full article