Search Results (388)

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of $R^2=0.697$ and $R^2=0.794$ for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops. Full article

Accurate, real-time monitoring of meat freshness is essential for reducing food waste and safeguarding consumer health, yet conventional methods rely on costly, laboratory-grade spectroscopy or destructive analyses. This work presents a low-cost electronic-nose platform that integrates a compact array of metal-oxide gas sensors (Figaro TGS2602, TGS2603, and Sensirion SGP30) with a Gaussian Process Regression (GPR) model to estimate a continuous freshness index under refrigerated storage. The pipeline includes headspace sensing, baseline normalization and smoothing, history-window feature construction, and probabilistic prediction with uncertainty. Using factorial analysis and response-surface optimization, we identify history length and sampling interval as key design variables; longer temporal windows and faster sampling consistently improve accuracy and stability. The optimized configuration (≈143-min history, ≈3-min sampling) reduces mean absolute error from ~0.51 to ~0.05 on the normalized freshness scale and shifts the error distribution within specification limits, with marked gains in process capability and yield. Although it does not match the analytical precision or long-term robustness of spectrometric approaches, the proposed system offers an interpretable and energy-efficient option for short-term, laboratory-scale monitoring under controlled refrigeration conditions. By enabling probabilistic freshness estimation from low-cost sensors, this GPR-driven e-nose demonstrates a proof-of-concept pathway that could, after further validation under realistic cyclic loads and operational disturbances, support more sustainable meat management in future smart refrigeration and cold-chain applications. This study should be regarded as a methodological, laboratory-scale proof-of-concept that does not demonstrate real-world performance or operational deployment. The technical implications described herein are hypothetical and require extensive validation under realistic refrigeration conditions. Full article

The global food industry is undergoing a critical shift toward sustainability, driven by high postharvest losses—reaching up to 40% for fruits and vegetables—and the need to reduce environmental impact. Sustainable postharvest innovations focus on improving quality, extending shelf life, and minimizing waste through eco-efficient technologies. Advances in non-thermal and minimal processing, including ultrasound, pulsed electric fields, and edible coatings, support nutrient preservation and food safety while reducing energy consumption. Although integrated postharvest technologies can reduce deterioration and microbial spoilage by 70–92%, significant challenges remain, including global losses of 20–40% and the high implementation costs of certain nanostructured materials. Simultaneously, eco-friendly packaging solutions based on biodegradable biopolymers and bio-composites are replacing petroleum-based plastics and enabling intelligent systems capable of monitoring freshness and detecting spoilage. Energy-efficient storage, smart sensors, and optimized cold-chain logistics further contribute to product integrity across distribution networks. In parallel, the circular bioeconomy promotes the valorization of agro-food by-products through the recovery of bioactive compounds with antioxidant and anti-inflammatory benefits. Together, these integrated strategies represent a promising pathway toward reducing postharvest losses, supporting food security, and building a resilient, environmentally responsible fresh produce system. Full article

Saltwater intrusion increasingly jeopardizes groundwater in low-lying coastal plains worldwide, where the combined effects of sea-level rise, land subsidence, and hydraulic regulation further exacerbate aquifer vulnerability and threaten the long-term sustainability of freshwater supplies. To move beyond sparse and fragmented piezometric observations, we propose “integrated coastal supersites”: wells equipped with multiparametric sensors and multilevel piezometers that couple high-resolution vertical conductivity–temperature–depth (CTD) profiling with continuous hydro-meteorological time series to monitor the hydrodynamic behavior of coastal aquifers and saltwater intrusion. This study describes the installation of two supersites and presents early insights from the first monitoring period, which, despite a short observation window limited to the summer season (July–September 2025), demonstrate the effectiveness of this approach. Two contrasting supersites were deployed in the coastal plain between the Brenta and Adige Rivers (Italy): Gorzone, characterized by a thick, laterally persistent aquitard, and Buoro, where the aquitard is thinner and discontinuous. Profiles and fixed sensors at both sites reveal a consistent fresh-to-saline transition in the phreatic aquifers and a secondary freshwater lens capping the confined systems. At Gorzone, the confining layer hydraulically isolates the deeper aquifer, preserving low salinity beneath a saline, tidally constrained phreatic zone. Groundwater heads oscillate by about 0.2 m, and rainfall events do not dilute salinity; instead, pressure transients—amplified by drainage regulation and inland-propagating tides—induce short-lived EC increases via upconing. Buoro shows smaller water-level variations, not always linked to rainfall, and, in contrast, exhibits partial vertical connectivity and faster dynamics: phreatic heads respond chiefly to internal drainage and local recharge, with rises rapidly damped by pumping, while salinity remains steady without episodic peaks. The confined aquifer shows buffered, delayed responses to surface forcings. Although the monitoring window is currently limited to 2025 through the summer season, these results offer compelling evidence that coastal supersites are reliable, scalable, and management-critical relevance platforms for groundwater calibration, forecasting, and long-term assessment. Full article

Precision agriculture, including sensors and artificial intelligence, is transforming agricultural monitoring. This study developed predictive models for fresh and dry forage mass, canopy height, forage density, dry matter (%DM), and crude protein (%CP) concentrations in signalgrass [Urochloa decumbens (Stapf) R.D. Webster] pastures using machine learning and UAV-based multispectral imagery. The experiment was conducted at the Federal University of Viçosa (2019–2020), applying nitrogen doses after each harvest to promote variability. Multiple Linear Regression (MLR), Support Vector Regressor (SVR), and Random Forest Regressor (RFR) models were trained with multispectral and meteorological data. The best results were obtained for fresh forage mass with RFR (R² = 0.82, RMSE = 2894.10 kg ha⁻¹), dry forage mass with SVR (R² = 0.68, RMSE = 719.87 kg ha⁻¹), and dry matter concentration with MLR (R² = 0.64, RMSE = 3.83%). Forage density showed moderate performance (R² = 0.56), while canopy height demonstrated limited accuracy (R² = 0.44). Crude protein was not adequately predicted by any model, highlighting multispectral sensor limitations and suggesting hyperspectral sensors usage. Results demonstrate the applicability of remote sensing combined with machine learning in forage management, but indicate the need to expand temporal and spatial data variability and integrate different sensor types to increase model robustness. Full article

The long-term reliability of catalytic gas sensors is strongly influenced by changes in the chemical state and cleanliness of the catalyst surface. In this work, we investigate the surface composition and stability of the platinum (Pt) nanoparticle catalytic layer in Gas Metal-Oxide-Semiconductor (GMOS) sensors under varying environmental conditions. Using X-ray Photoelectron Spectroscopy (XPS) and High-Resolution (HR) XPS, we compared fresh, aged samples, thermally treated samples, and samples stored with or without a mechanical filter. The results show that prolonged ambient storage leads to the accumulation of adventitious carbon and nitrogen-containing species, as well as partial oxidation of platinum, which reduces the number of active metallic Pt sites. Thermal treatment at 300 °C for 30 min restores metallic Pt exposure by removing surface contaminants and narrowing the Pt 4f peaks. However, recontamination occurs during subsequent storage, with significant differences depending on surface protection. Sensors equipped with a mechanical filter exhibited obvious Pt metallic peaks in HR-XPS analysis, with lower carbon and nitrogen levels, compared to unprotected samples. These findings demonstrate that while heating refreshes catalytic activity, long-term stability requires complementary filtration to prevent re-adsorption of airborne species. The combined approach of heating and filtration is thus essential to ensure reliable performance of GMOS sensors for indoor and outdoor air quality monitoring. Full article

Monitoring the freshness of perishable foods remains a challenge due to the lack of simple and reliable indicators that can visually reflect chemical and microbial changes. In this study, a colorimetric freshness indicator was developed using bromocresol green (BCG) and bromocresol purple (BCP), two pH-sensitive dyes with complementary transition ranges, to provide a visible and quantitative response corresponding to beef quality during cold storage. Cellulose acetate (CA) films were prepared by incorporating the dyes with different plasticizers—glycerol and polyethylene glycol (PEG 200 and PEG 400)—at varying ratios, resulting in 24 formulations. Based on color stability and sensitivity to trimethylamine (TMA) vapor, two optimized indicators were selected for further packaging tests with beef samples stored at 4 °C. Beef freshness was evaluated by total bacterial count (TBC), total volatile basic nitrogen (TVB-N), and pH, while volatile amines in the headspace were quantified using solid-phase microextraction–gas chromatography–flame ionization detection (SPME–GC–FID). The color difference (ΔE) of the indicators showed strong correlations with TBC and TVB-N, and a threshold of ΔE ≈ 12 provided a practical visual cue corresponding to the microbiological safety limit. The two indicators exhibited complementary functions, with G100-1 acting as an early-warning sensor and G100-2 maintaining contrast at later stages. These findings demonstrate the potential of this dual-indicator system as a simple, non-destructive tool for intelligent packaging applications. Full article

Pleurotus ostreatus is valued for its nutritional, medicinal, economic, and ecological benefits and is widely used in the food, pharmaceutical, and environmental protection industries. Pleurotus ostreatus, as a highly perishable edible fungus, faces significant challenges in supply chain quality control and food safety due to its short shelf life. As consumer demand for food freshness and full traceability increases, there is an urgent need to establish a reliable traceability system that enables real-time monitoring, spoilage prevention, and quality assurance. This study focuses on the Pleurotus ostreatus supply chain and designs and implements a multi-role flexible traceability system that integrates blockchain and the Internet of Things. The system collects key production and storage environment parameters in real time through sensor networks and enhances data accuracy and robustness using an improved adaptive weighted fusion algorithm, enabling precise monitoring of the growth environment and quality risks. The system adopts a “link-chain” mapping mechanism for multi-chain storage and dynamic reorganization of business processes. It incorporates attribute-based encryption strategies and smart contracts to support tiered data access and secure sharing among multiple parties. Key information is stored on the blockchain to prevent tampering, while auxiliary data is stored in off-chain databases and the Interplanetary File System to ensure efficient and verifiable data queries. Deployed at Shandong Qihe Ecological Agriculture Co., Ltd., No. 517, Xilou Village, Kunlun Town, Zichuan District, 255000, Zibo City, Shandong Province, China, the system covers 12 cultivation units and 60 sensor nodes, recording over 50,000 traceable data points. Experimental results demonstrate that the system outperforms baseline methods in query latency, data consistency, and environmental monitoring accuracy. The improved fusion algorithm reduced the total variance of environmental data by 20%. In practical application, the system reduced the spoilage rate of Pleurotus ostreatus by approximately 12.3% and increased the quality inspection pass rate by approximately 15.4%, significantly enhancing the supply chain’s quality control and food safety capabilities. The results show that the framework is feasible and scalable in terms of information credibility and operational efficiency and significantly improves food quality and safety monitoring throughout the production, storage, and distribution of Pleurotus ostreatus. This study provides a viable technological path for spoilage prevention, quality tracking, and digital food safety supervision, offering valuable insights for both food science research and practical applications. Full article

We developed and evaluated a compact mushroom fruiting chamber equipped with Internet of Things technologies, designed to support decentralized urban agriculture. The system was constructed from a retrofitted glass-door refrigerator and integrated with Internet-connected sensors and a custom microcontroller to monitor and regulate temperature and humidity continuously. The control unit managed key variables, including temperature and relative humidity, during the cultivation of Pleurotus ostreatus mushrooms. Experimental trials assessed the effectiveness of the IoT-based system in maintaining optimal growth conditions by dynamically adjusting parameters tailored to the fungus’s specific physiological requirements during fruiting. The prototype successfully maintained a stable cultivation environment, achieving an average temperature of 25.0 °C (±0.7 °C) and relative humidity of 90% (±8%). Under optimized conditions (18 °C, with the cultivation block plastic cover preserved), mushroom yield reached 230 ± 2 g per block, corresponding to a biological efficiency of 44% and an estimated productivity of up to 612.04 kg m⁻² per year. Furthermore, the system achieved a water footprint of only 4.39 L kg⁻¹ of fresh mushrooms, significantly lower than that typically reported for conventional cultivation methods. These results demonstrate the feasibility of an efficient, compact, and water-saving controlled environment for mushroom cultivation, enabled by IoT-based technologies and organic residue substrates. Remote monitoring and control capabilities support urban food security, reduce transport-related emissions, optimize water use, and promote sustainable practices within a circular economy framework. The system’s adaptability suggests potential scalability to other crops and urban agricultural contexts. Full article

A multi-factor analysis of cultivar, biofertiliser, and growing season was conducted to optimise lettuce agronomic and quality traits in diverse soil conditions. The goal was to identify soil differences and offer practical recommendations to improve lettuce traits and quality for farmers and the processing industry. The study employed a complete block design with four treatments, three involving biofertilisers, applied to six lettuce cultivars grown in two contrasting soil types- Mollic Gleysol (Calcaric)-GL and Hortic Anthrosol (Terric, Transportic)-AT, across three consecutive greenhouse seasons (autumn, winter, and spring). Biofertilisers were applied to the soil before transplanting and foliarly during the growing cycle, with four of the following treatments: control (no fertilisation), a fertiliser containing beneficial microorganisms, a Trichoderma-based fertiliser, and a combination of both. In GL soil, all biofertiliser treatments increased rosette height, leaf number, and stem length, whereas in AT soil, all morphological parameters declined significantly. The green cultivars ‘Aquino’ and ‘Kiribati’ showed superior morphological performance, particularly in spring and winter. Rosette fresh weight, a key indicator of plant biomass, reached 236.4 g in ‘Aquino’ grown in GL soil, and 208.6 g in ‘Kiribati’ grown in AT soil. Dualex™ leaf sensor measurement indicated that ‘Aquino’ exhibited the highest nitrogen balance index (NBI), while the red cultivar ‘Gaugin’ recorded the highest chlorophyll, flavonoid, and anthocyanin contents. Combined fertilisers increased NBI by 6.3% and chlorophyll by 6.8% in GL soil. Trichoderma fertiliser alone raised NBI by 6.8% in GL soil, whereas in AT soil, plants accumulated more flavonoids and anthocyanins (by 9.2% and 8.5%). Optical parameters were highest in autumn. The three-factor experiment demonstrated that cultivar, biofertiliser, and growing season significantly influenced the majority of measured traits. Correlation analysis revealed that rosette fresh weight was positively associated with NBI but negatively correlated with quality-related traits. Based on these findings, cultivars ‘Aquino’, ‘Kiribati’, and ‘Gaugin’ are recommended for both farmers and the processing industry to improve lettuce production quantity and quality. Overall, cultivar, biofertiliser, and season strongly influenced the measured parameters, underscoring the importance of tailoring biofertiliser application to soil type and season to achieve optimal production outcomes. Full article

This study aimed to assess the potential of machine learning models applied to high spatial resolution images from UAVs for estimating the aboveground biomass (AGB) of forage grass cultivated in the Brazilian semiarid region. The fresh and dry AGB were determined in Cenchrus ciliare plots with an area of 0.04 m². Spectral data were obtained using a multispectral sensor (Red, Green, and NIR) mounted on a UAV, from which 45 vegetation indices were derived, in addition to a structural variable representing plant height (H95). Among these, H95, GDVI, GSAVI2, GSAVI, GOSAVI, GRDVI, and CTVI exhibited the strongest correlations with biomass. Following multicollinearity analysis, eight variables (R, G, NIR, H95, CVI, MCARI, RGR, and Norm G) were selected to train Random Forest (RF), Support Vector Machine (SVM), and XGBoost models. RF and XGBoost yielded the highest predictive performance, both achieving an R² of 0.80 for AGB—Fresh. Their superiority was maintained for AGB—Dry estimation, with R² values of 0.69 for XGBoost and 0.67 for RF. Although SVM produced higher estimation errors, it showed a satisfactory ability to capture variability, including extreme values. In modeling, the incorporation of plant height, combined with spectral data obtained from high spatial resolution imagery, makes AGB estimation models more reliable. The findings highlight the feasibility of integrating UAV-based remote sensing and machine learning algorithms for non-destructive biomass estimation in forage systems, with promising applications in pasture monitoring and agricultural land management in semi-arid environments. Full article

Real-time monitoring of atmospheric emissions is critical for ensuring transparency, compliance, and rapid response to environmental risks. However, traditional systems often suffer from latency and a lack of verifiable data integrity. This paper presents AABF+, an adaptive packetization and microblock model built on a permissioned blockchain that supports intelligent emissions monitoring. The proposed system dynamically groups sensor readings into microblocks and commits them using Byzantine Fault Tolerant (BFT) consensus, enabling both high throughput and verifiable traceability. Unlike fixed-window blockchains, AABF+ adapts the microblock size and time window based on incoming data rates, balancing responsiveness and reliability. The model was implemented and experimentally evaluated in an edge-class 1 GbE testbed under real MRV (Measurement–Reporting–Verification) conditions. Results show that AABF+ achieves a median end-to-end latency of 0.96 s for single-record transactions and 3.07 s for 1000-record batches, while maintaining strong cryptographic verification of all entries. These findings demonstrate that AABF+ provides second-level data freshness with verifiable provenance, offering a practical foundation for digital environmental governance and regulatory compliance in Industry 4.0 ecosystems. Full article

Nanotechnology offers powerful new tools to enhance food quality monitoring and safety assurance. In the food industry, nanoscale materials (e.g., metal, metal oxide, carbon, and polymeric nanomaterials) are being integrated into sensory systems to detect spoilage, contamination, and intentional food tampering with unprecedented sensitivity. Nanosensors can rapidly identify foodborne pathogens, toxins, and chemical changes that signal spoilage, overcoming the limitations of conventional assays that are often slow, costly, or require expert operation. These advances translate into improved food safety and extended shelf-life by allowing early intervention (for example, via antimicrobial nano-coatings) to prevent spoilage. This review provides a comprehensive overview of the types of nanomaterials used in food sensory applications and their mechanisms of action. We examine current applications in detecting food spoilage indicators and adulterants, as well as recent innovations in smart packaging and continuous freshness monitoring. The advantages of nanomaterials—including heightened analytical sensitivity, specificity, and the ability to combine sensing with active preservative functions—are highlighted alongside important toxicological and regulatory considerations. Overall, nanomaterials are driving the development of smarter food packaging and sensor systems that promise safer foods, reduced waste, and empowered consumers. However, realizing this potential will require addressing safety concerns and establishing clear regulations to ensure responsible deployment of nano-enabled food sensing technologies. Representative figures of merit include Au/AgNP melamine tests with LOD 0.04–0.07 mg L⁻¹ and minute-scale readout, a smartphone Au@carbon-QD assay with LOD 3.6 nM, Fe₃O₄/DPV detection of Sudan I at 0.001 µM (linear 0.01–20 µM), and a reusable Au–Fe₃O₄ piezo-electrochemical immunosensor for aflatoxin B1 with LOD 0.07 ng mL⁻¹ (≈15 × reuse), alongside freshness labels that track TVB-N/amine in near-real time and e-nose arrays distinguishing spoilage stages. Full article

The meat industry faces significant economic losses and environmental impacts due to spoilage and waste, much of which results from inadequate, delayed, or inefficient quality assessment. Traditional methods used for assessing meat quality are often time-consuming, labor-intensive, and lack the ability to provide real-time information, making them insufficient for modern supply chains that demand safety, freshness, and minimal waste. Recent advances in nanotechnology position nanofibers (NFs) as promising materials for addressing these challenges through smart sensing and active packaging. NFs, characterized by their high surface-to-volume ratio, tunable porosity, and small diameter, enable superior encapsulation and immobilization of sensing agents. These features improve the efficiency of colorimetric indicators, electronic noses, biosensors and time–temperature indicators. Electrospun NFs functionalized with metallic nanoparticles can detect contaminants such as antibiotics and hormones, while polymeric NFs embedded with reduced graphene oxide act as electrodes for advanced biosensing. Freshness indicators based on pH and nitrogenous compounds demonstrate real-time spoilage detection through visible color changes. This review explores nanofiber fabrication methods, their integration into sensing systems, and their potential to advance rapid, sustainable, and cost-effective meat quality monitoring. Full article

The detection and monitoring of ions are essential for a broad range of applications, including industrial process control and biomedical diagnostics. Traditional ion-sensitive field-effect transistors require bulky and expensive reference electrodes, which face several limitations, including device miniaturization, high fabrication costs, and incompatibility with semiconductor manufacturing processes. Here, we introduce a reference-less semiconductor ion sensor (RELESIS) that utilizes anodic aluminum oxide film as both the sensitive and dielectric layer. The RELESIS is composed of a metal-oxide-semiconductor field-effect transistor and an interdigital electrode, which fundamentally eliminates the need for a reference electrode, thereby enabling device miniaturization. During fabrication, the anodic oxidation process is employed in place of the expensive atomic layer deposition method, significantly reducing manufacturing costs while maintaining high surface quality. In practical measurements, the RELESIS device demonstrated an excellent pH sensitivity of 57.8 mV/pH with a low hysteresis of 7 mV. As a proof-of-concept application, the RELESIS device was employed for real-time, non-destructive monitoring of milk freshness, accurately detecting pH changes from fresh to spoiled in milk samples. The combination of reference-less structure, low-cost fabrication, and superior sensing performance positions this technology as a promising platform for next-generation portable ion sensing systems in food safety, environmental monitoring, and point-of-care diagnostics. Full article