Search Results (251)

The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [$R^2$] = 0.75) and potassium (K) ($R^2$ = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) ($R^2$ = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture. Full article

This research examines the effect of annealing temperature on the growth orientation of zinc oxide (ZnO) nanorods and its subsequent influence on NO₂ gas sensing efficiency. Zinc oxide (ZnO) nanorods were synthesized using the chemical bath deposition method, followed by annealing at 300, 400, and 500 °C. Diffraction analysis confirmed that both non-annealed and annealed ZnO nanorods crystallize in a hexagonal wurtzite structure. However, increasing the annealing temperature shifts the growth orientation from the c-axis (002) toward the (100) and (101) directions. Microscopy images (FE-SEM) revealed a reduction in nanorod diameter as the annealing temperature increases. Optical characterization using UV–visible and photoluminescence spectroscopy indicated shifts in the band gap energy and emission properties. Contact angle measurements demonstrated the hydrophobic nature of the films. Gas sensing tests at 200 °C revealed that the ZnO thin film annealed at 400 °C achieved the highest NO₂ response of 5.88%. The study highlights the critical role of annealing in modifying the crystallinity, growth orientation, and defect states of ZnO thin films, ultimately enhancing their NO₂ detection capability. Full article

This article presents a comprehensive overview of recent advancements in photonic crystal fiber (PCF)-based sensors, with a particular focus on the surface plasmon resonance (SPR) phenomenon for biosensing. With their ability to modify core and cladding structures, PCFs offer exceptional control over light guidance, dispersion management, and light confinement, making them highly suitable for applications in refractive index (RI) sensing, biomedical imaging, and nonlinear optical phenomena such as fiber tapering and supercontinuum generation. SPR is a highly sensitive optical phenomenon, which is widely integrated with PCFs to enhance detection performance through strong plasmonic interactions at metal–dielectric interfaces. The combination of PCF and SPR technologies has led to the development of innovative sensor geometries, including D-shaped fibers, slotted-air-hole structures, and internal external metal coatings, each optimized for specific sensing goals. These PCF-SPR-based sensors have shown promising results in detecting biomolecular targets such as excess cholesterol, glucose, cancer cells, DNA, and proteins. Furthermore, this review provides an in-depth analysis of key design parameters, plasmonic materials, and sensor models used in PCF-SPR configurations, highlighting their comparative performance metrics and application prospects in medical diagnostics, environmental monitoring, and chemical analysis. Thus, an exhaustive analysis of various sensing parameters, plasmonic materials, and sensor models used in PCF-SPR sensors is presented and explored in this article. Full article

This study provides a detailed comparison of three optical sensing approaches for colourimetric bio/chemical detection, focusing on cost, scalability, and performance. We examine laboratory-grade spectrophotometry, portable camera-based imaging, and low-cost LED photometry using Paired Emitter–Detector Diode (PEDD) charge–discharge methodology. Our findings reveal that while the LED-based PEDD system outperforms the other two methods in key sensory metrics—such as sensitivity, resolution, and limit of detection—its cost-effectiveness and scalability make it a promising solution for widespread industrial and field applications. Compared to the spectrophotometer, the LED/PEDD approach demonstrates improvements in measurement range (×16.39), dynamic range (×147.06), accuracy (×1.79), and sensitivity (×107.53). The results highlight the potential for industrial-scale adoption of LED photometry, especially for cost-effective applications in bio/chemical sensing sectors. Full article

Soil health has become increasingly important in recent years. The Hokkaido government initiated its original administrative strategy referred to as “Clean Agriculture” in 1991, before the concept of soil health and soil quality evolved in the 1990s. Also, Clean Agriculture has been integrated with remote sensing techniques for spatial application in arable fields. In this review paper, we summarized the scientific progress in Clean Agriculture and the management of soil health using remote sensing. One of the main pillars of Clean Agriculture is the minimal usage of chemical fertilizers and agrochemicals to increase soil fertility through the proper application of organic matter. The other two pillars are the sustainment and enhancement of the natural recycling function in agriculture and the enhancement of a stable production safe and high-quality agricultural products taking into account environmental harmony. These agronomic practices can increase soil fertility, maintain water quality, mitigate climate change, and maintain human health, and are similar to those in North America and the EU. Moreover, soil nitrogen fertility evaluated by autoclaved nitrogen (AC-N) can be estimated in large-scale fields and areas via remote sensing, which can facilitate variable nitrogen fertilization using variable-rate planters or broadcasters. Furthermore, systems comprising the growth sensor and variable-rate broadcaster can determine the additional nitrogen fertilization rates for winter wheat on the fields, which enhances soil health over relatively large areas. Further research is needed to expand the spatial utility of various Clean Agriculture techniques using multiperiod satellite images. Full article

Measuring the composition of materials at a distance is a key requirement in industrial process monitoring, recycling, precision agriculture, and environmental monitoring. Spectral imaging in the visible or near-infrared (NIR) spectral bands provides a potential solution by combining spatial and spectral information, and its application has seen significant growth over recent decades. Low-cost solutions for visible multispectral imaging (MSI) have been developed due to the widespread availability of silicon detectors, which are sensitive in this spectral region. In contrast, development in the NIR has been slower, primarily due to the high cost of indium gallium arsenide (InGaAs) detector arrays required for imaging. This work aims to bridge this gap by introducing a standoff material sensing concept which combines spatial and spectral resolution without the hardware requirements of traditional spectral imaging systems. It combines spatial imaging in the visible range with a CMOS camera and NIR spectral measurement at selected points of the scene using an NIR spectral sensor. This allows the chemical characterization of different objects of interest in a scene without acquiring a full spectral image. We showcase its application in plastic classification, a key functionality in sorting and recycling systems. The system demonstrated the capability to classify visually identical plastics of different types in a standoff measurement configuration and to produce spectral measurements at up to 100 points in a scene. Full article

Remote sensing imaging technology is one of the safest and most effective tools for gas leakage monitoring in chemical parks, as it enables fast and accurate access to detailed information about the gas cloud (e.g., volume, distribution, diffusion, and location) in the case of gas leakage. While multi-spectral imaging systems are commonly used for hazardous gas leakage detection, efforts to realize the three-dimensional reconstruction of gas clouds through data obtained from multi-spectral imaging systems remain scarce. In this study, we propose a method for realizing the three-dimensional reconstruction of gas clouds with only two multi-spectral imaging systems; in particular, the two multi-spectral imaging systems are used to simultaneously observe the three-dimensional space with gas leakage and reconstruct gas cloud images in real time. A geometric method is used for the localization in the monitoring space and the construction of a three-dimensional spatial grid. The non-axisymmetric inverse Abel transform (IAT) is then applied to the extracted gas absorbance images in order to realize the reconstruction of each layer, and these are then stacked to form a 3D gas cloud. Through the above measurement, identification, and reconstruction processes, a 3D gas cloud with geometric information and concentration distribution characteristics is generated. The results of simulation experiments and external field tests prove that gas clouds can be localized under the premise that they are completely covered by the field of view of both scanning systems, and the 3D distribution of the leakage gas cloud can be reconstructed quickly and accurately with the proposed system. Full article

Multi-focal microlens arrays provide notable advantages over mono-focal counterparts, such as multi-scale imaging capabilities and optical aberration correction. However, existing multi-focal microlens arrays fabricated on continuous surfaces are incapable of achieving confocal imaging. As a result, multiple focus adjustments are required to acquire comprehensive image data, thereby complicating system design and increasing operational duration. To overcome this limitation, a stepped confocal surface microlens array is proposed, capable of simultaneously capturing images with multiple depths of field, various field-of-view scales, and different resolutions—without the need for additional focus adjustments. A combination of femtosecond laser processing and chemical etching was employed to fabricate microlenses with varying curvatures on a stepped fused silica substrate, which was subsequently used as a mold. The final stepped confocal microlens array was replicated via polydimethylsiloxane (PDMS) molding. Preliminary experimental analyses were carried out to determine the relationship between processing parameters and the resulting focal lengths. By precisely controlling these parameters, the fabricated stepped confocal microlens array successfully enabled confocal imaging, allowing for the simultaneous acquisition of diverse image data. This microlens array shows great potential in advancing lightweight, integrated, and highly stable optical systems for applications in optical sensing, spatial positioning, and machine vision. Full article

Rare-earth-doped fluoride nanoparticles (NPs), known for their tunable luminescence and high chemical stability, hold significant potential for applications in X-ray imaging and radiation dose monitoring. However, most research has primarily focused on lanthanide-doped NaLuF₄ or NaYF₄ nanosystems. In this work, Cs₂NaYF₆:Tb NPs with enhanced X-ray excited optical luminescence (XEOL) intensity were developed. Our results indicate that low oleic acid (OA) content and a high [Cs⁺]/[Na⁺] ratio favor the formation of pure cubic-phase Cs₂NaYF₆:Tb NPs. Cs₂NaYF₆:Tb NPs were successfully fabricated into thin films and employed as nanoscintillator screens for X-ray imaging, achieving a high spatial resolution of 20.0 Lp/mm. Beyond X-ray imaging applications, Cs₂NaYF₆:Tb NPs were also explored for spermine detection, demonstrating high sensitivity with a detection limit of 0.44 μM (under X-ray excitation) within a concentration range of 0–60 μM. These findings may contribute to the development of novel lanthanide-doped fluoride nanoscintillators for high-performance X-ray imaging and molecular sensing. Full article

Fluorescent sensors are indispensable tools in fields such as molecular biology, clinical diagnostics, biotechnology, and environmental monitoring, due to their high sensitivity, selectivity, biocompatibility, rapid response, and ease of use. However, conventional fluorophores often suffer from aggregation-caused quenching (ACQ), leading to diminished fluorescence in the aggregated state. The advent of aggregation-induced emission (AIE) luminogens, which exhibit enhanced fluorescence upon aggregation, offers a powerful solution to this limitation. Their unique photophysical properties have made AIE-based materials highly valuable for diverse applications, including biomedical imaging, optoelectronics, stimuli-responsive systems, drug delivery, and chemical sensing. Notably, AIE-based fluorescent probes are emerging as attractive alternatives to traditional analytical methods owing to their low cost, fast detection, and high selectivity. Over the past two decades, considerable progress has been made in the rational design and development of AIE-active small-molecule fluorescent probes for detecting a wide variety of analytes, such as biologically relevant molecules, drug compounds, volatile organic compounds (VOCs), explosives, and contaminants associated with forensic and food safety analysis. This review highlights recent advances in organic AIE-based fluorescent probes, beginning with the fundamentals of AIE and typical “turn-on” sensing mechanisms, and concluding with a discussion of current challenges and future opportunities in this rapidly evolving research area. Full article

Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging long-term ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats. Full article

Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil’s physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (−0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices. Full article

Future climate conditions may jeopardize the suitability of traditional grape-growing areas in the Mediterranean. However, precise vineyard management is a crucial component of adaptation strategies aimed at optimizing resource efficiency, which is essential for sustainable farming practices. A fine-scale characterization, based on the spatial variability of soil’s physical–chemical and hydrological traits combined with temporal variability of vine canopy temperature extracted from UAV thermal images has been adopted in a rainfed vineyard of central Italy, for better understanding the impact of soil and climate abiotic factors in the vineyard for planning precision adaptation strategies encouraging sustainable resource use. This study identifies significant soil heterogeneity within the tested vineyard, affecting water retention, nutrient availability, and vine water stress. We combined ground-based measurements with remote sensing-enhanced data spatialization and helped to advocate for site-specific management techniques as short- and long-term strategies (such as canopy management, deficit irrigation, and compost application) to counter climate emergencies, restore soil health, and preserve vine function and economic yields. Full article

The current Common Agriculture Policy (CAP) foresees a reduction of 50% in the use of herbicides by 2030. This study investigates the potential of integrating remote sensing with a low-cost RGB sensor and variable-rate technology (VRT) to optimize herbicide application in a ryegrass (Lolium multiflorum Lam.) fodder crop. The trial was conducted on three 7.5-hectare plots, comparing a variable-rate application (VRA) of herbicide guided by a prescription map generated from segmented digital images, with a fixed-rate application (FRA) and a control (no herbicide applied). The weed population and crop biomass were assessed to evaluate the efficiency of the proposed method. Results revealed that the VRA method reduced herbicide usage by 30% (0.22 l ha⁻¹) compared to the FRA method, while maintaining comparable crop production. These findings demonstrate that smart weed management techniques can contribute to the CAP’s sustainability goals by reducing chemical inputs and promoting efficient crop production. Future research will focus on improving weed recognition accuracy and expanding this methodology to other cropping systems. Full article

Water quality in aquaculture has a direct impact on the growth and development of the aquatic organisms being cultivated. The rapid, accurate and comprehensive control of water quality in aquaculture ponds is crucial for the management of aquaculture water environments. Traditional water quality monitoring methods often use manual sampling, which is not only time-consuming but also reflects only small areas of water bodies. In this study, unmanned aerial vehicles (UAV) equipped with high-spectral cameras were used to take remote sensing images of experimental aquaculture ponds. Concurrently, we manually collected water samples to analyze critical water quality parameters, including total nitrogen (TN), ammonia nitrogen (NH₄⁺-N), total phosphorus (TP), and chemical oxygen demand (COD). Regression models were developed to assess the accuracy of predicting these parameters based on five preprocessing techniques for hyperspectral image data (L2 norm, Savitzky–Golay, first derivative, wavelet transform, and standard normal variate), two spectral feature selection methods were utilized (successive projections algorithm and competitive adaptive reweighted sampling), and three machine learning algorithms (extreme learning machine, support vector regression, and eXtreme gradient boosting). Additionally, a deep learning model incorporating the full spectrum was constructed for comparative analysis. Ultimately, according to the determination coefficient (R²) of the model, the optimal prediction model was selected for each water quality parameter, with R² values of 0.756, 0.603, 0.94, and 0.858, respectively. These optimal models were then utilized to visualize the spatial concentration distribution of each water quality parameter within the aquaculture district, and evaluate the rationality of the model prediction by combining manual detection data. The results show that UAV hyperspectral technology can rapidly reverse the spatial distribution map of water quality of aquaculture ponds, realizing rapid and accurate acquisition for the quality of aquaculture water, and providing an effective method for monitoring aquaculture water environments. Full article